CN113920492A - Method and device for detecting people in vehicle, electronic equipment and storage medium - Google Patents
Method and device for detecting people in vehicle, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN113920492A CN113920492A CN202111272801.XA CN202111272801A CN113920492A CN 113920492 A CN113920492 A CN 113920492A CN 202111272801 A CN202111272801 A CN 202111272801A CN 113920492 A CN113920492 A CN 113920492A
- Authority
- CN
- China
- Prior art keywords
- occupant
- passenger
- child
- face
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims description 55
- 238000001514 detection method Methods 0.000 claims abstract description 100
- 230000001815 facial effect Effects 0.000 claims description 27
- 238000004590 computer program Methods 0.000 claims description 14
- 230000004044 response Effects 0.000 claims description 12
- 230000002996 emotional effect Effects 0.000 claims description 10
- 238000000605 extraction Methods 0.000 claims description 5
- 238000010586 diagram Methods 0.000 description 20
- 238000012545 processing Methods 0.000 description 20
- 238000004891 communication Methods 0.000 description 10
- 230000008569 process Effects 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 9
- 230000003287 optical effect Effects 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000013136 deep learning model Methods 0.000 description 3
- 230000008451 emotion Effects 0.000 description 2
- 230000008909 emotion recognition Effects 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000010295 mobile communication Methods 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 229910044991 metal oxide Inorganic materials 0.000 description 1
- 150000004706 metal oxides Chemical class 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computational Linguistics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Evolutionary Biology (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Image Analysis (AREA)
Abstract
The present disclosure relates to an in-vehicle occupant detection method and apparatus, an electronic device, and a storage medium, which detect a child occupant inside a vehicle by acquiring a target image including the inside of the vehicle and according to the target image. And further extracting a face image of the child passenger from the target image, and determining whether the child passenger is an infant or not according to the face image of the child passenger.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for detecting a person in a vehicle, an electronic device, and a storage medium.
Background
During the running process of the vehicle, the vehicle is often bumpy due to uneven road or acceleration, braking and the like of the vehicle. When the vehicle bumps, the safety belt can protect the safety of adults and teenagers in the vehicle. However, because the size of the infant is small, if other protective measures are not taken, a series of potential safety hazards are generated. Therefore, it is necessary to detect whether or not an infant is present in the vehicle, and to indicate that a corresponding safety measure needs to be taken when the infant is present. In addition, due to the poor ability of an infant to perform autonomously, special care is required in a driving environment. Therefore, it is very necessary to detect the infant in the vehicle.
Disclosure of Invention
The disclosure provides a method and a device for detecting people in a vehicle, electronic equipment and a storage medium, and aims to judge whether an infant exists in the vehicle or not in a detection mode.
According to a first aspect of the present disclosure, there is provided an in-vehicle occupant detection method, including:
acquiring a target image of the interior of the vehicle;
detecting a child occupant inside the vehicle based on the target image;
extracting a face image of the detected child occupant from the target image;
determining whether the child occupant is an infant from the facial image of the child occupant.
In one possible implementation, the detecting a child occupant inside the vehicle based on the target image includes:
detecting an occupant inside the vehicle based on the target image;
for each detected occupant, it is determined whether the occupant is a child occupant.
In one possible implementation, the detecting an occupant inside a vehicle based on the target image includes:
carrying out face detection and human body detection based on the target image to obtain a face detection frame and a body detection frame of at least one passenger;
whether each occupant is a child occupant is determined based on the face detection frame and the body detection frame of each occupant.
In one possible implementation, the determining whether each occupant is a child occupant based on the face detection frame and the body detection frame of each occupant includes:
extracting a face image and/or a body image of each passenger from the target image based on a face detection frame and/or a body detection frame of each passenger;
and inputting the face image and/or the body image of each passenger into the trained child recognition model to obtain a detection result of whether each passenger is a child passenger.
In one possible implementation, the inputting the facial image and/or the body image of each passenger into the trained child recognition model includes:
for each passenger, in response to the face image of the passenger meeting a preset face visible condition or the body image of the passenger meeting a preset body visible condition, inputting the face image and/or the body image of the passenger into a trained child recognition model.
In a possible implementation manner, the face-visible condition includes that a ratio of a face-shielded area to a face area does not exceed a first preset value, and the body-visible condition includes that a ratio of a body-shielded area to a body area does not exceed a second preset value.
In a possible implementation manner, the first preset value is smaller than the second preset value.
In one possible implementation, the determining whether the child occupant is an infant from the facial image of the child occupant includes:
and under the condition that the face image of the child passenger is determined to meet the preset face visible condition, determining whether the child passenger is an infant or not according to the face image of the child passenger.
In one possible implementation, the meeting the preset face visibility condition of the facial image of the child occupant includes:
the ratio of the face shielding area to the face area does not exceed a first preset value.
In one possible implementation, the method further includes:
determining the position of a seat where the passenger is located according to the target image when the passenger is an infant;
and generating prompting seat changing information in response to that the seat position where the passenger is located is the front row.
In one possible implementation, the method further includes:
determining whether the passenger is on a safety seat in the rear row according to the target image when the passenger is an infant;
and generating safety prompt information when the passenger is not in the safety seat of the rear row.
In one possible implementation, the method further includes:
determining an emotional state according to facial features of the occupant if the occupant is an infant;
in response to the emotional state being a negative state, generating soothing information.
According to a second aspect of the present disclosure, there is provided an in-vehicle occupant detection apparatus including:
the image acquisition module is used for acquiring a target image in the vehicle;
a first detection module for detecting a child occupant inside the vehicle based on the target image;
the region extraction module is used for extracting a face image of the detected child passenger from the target image;
the second detection module is used for determining whether the child passenger is an infant or not according to the face image of the child passenger.
In one possible implementation manner, the first detection module includes:
an occupant identification sub-module for detecting an occupant inside the vehicle based on the target image;
and the child passenger judging submodule is used for judging whether the passenger is a child passenger or not aiming at each detected passenger.
In one possible implementation, the occupant identification submodule includes:
the detection frame identification unit is used for carrying out face detection and human body detection based on the target image to obtain a face detection frame and a body detection frame of at least one passenger;
an occupant identification unit for determining whether each occupant is a child occupant based on a face detection frame and a body detection frame of each occupant.
In one possible implementation, the occupant identification unit includes:
a region extraction subunit configured to extract a face image and/or a body image of each occupant from the target image based on a face detection frame and/or a body detection frame of each occupant;
and the model identification subunit is used for inputting the face image and/or the body image of each passenger into the trained child identification model to obtain the detection result of whether each passenger is a child passenger.
In one possible implementation, the model identification subunit includes:
and the condition judging unit is used for responding to that the face image of the passenger meets a preset face visible condition or the body image of the passenger meets a preset body visible condition for each passenger, and inputting the face image and/or the body image of the passenger into the trained child recognition model.
In a possible implementation manner, the face-visible condition includes that a ratio of a face-shielded area to a face area does not exceed a first preset value, and the body-visible condition includes that a ratio of a body-shielded area to a body area does not exceed a second preset value.
In a possible implementation manner, the first preset value is smaller than the second preset value.
In one possible implementation, the second detection module includes:
the infant judgment submodule is used for determining whether the child passenger is an infant or not according to the face image of the child passenger under the condition that the face image of the child passenger meets the preset face visible condition.
In one possible implementation, the meeting the preset face visibility condition of the facial image of the child occupant includes:
the ratio of the face shielding area to the face area does not exceed a first preset value.
In one possible implementation, the apparatus further includes:
the position identification module is used for determining the position of a seat where the passenger is located according to the target image under the condition that the passenger is an infant;
and the first information generation module is used for generating prompting seat changing information in response to the situation that the seat where the passenger is located is the front row.
In one possible implementation, the apparatus further includes:
a seat identification module for determining whether the passenger is on a safety seat in the rear row according to the target image when the passenger is an infant;
and the second information generation module is used for generating safety prompt information when the passenger is not positioned on the safety seat at the rear row.
In one possible implementation, the apparatus further includes:
the emotion recognition module is used for determining an emotion state according to facial features of the passenger under the condition that the passenger is an infant;
and the third information generation module is used for responding to the emotional state as a negative state and generating placating information.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.
The disclosed embodiments detect a child occupant inside a vehicle by acquiring a target image including the inside of the vehicle and detecting the child occupant from the target image. And further extracting a face image of the child passenger from the target image, and determining whether the child passenger is an infant or not according to the face image of the child passenger. According to the embodiment of the invention, the child passengers are primarily screened in the target image, and then the infant passengers are screened according to the face image of the child passengers, so that the accurate detection of the infant passengers in the vehicle is realized.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
FIG. 1 shows a flow chart of a method of in-vehicle occupant detection according to an embodiment of the present disclosure;
FIG. 2 shows a schematic diagram of an in-vehicle occupant detection process according to an embodiment of the present disclosure;
FIG. 3 illustrates a schematic diagram of a facial image of a child occupant in accordance with an embodiment of the present disclosure;
FIG. 4 shows a schematic diagram of an in-vehicle occupant detection apparatus according to an embodiment of the present disclosure;
FIG. 5 shows a schematic diagram of an electronic device according to an embodiment of the present disclosure;
fig. 6 shows a schematic diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
Fig. 1 shows a flow chart of a method for detecting an occupant in a vehicle according to an embodiment of the present disclosure. In a possible implementation manner, the in-vehicle person detection method may be executed by an electronic device such as a terminal device or a server. The terminal device may be a fixed or mobile vehicle-mounted device built in a vehicle, or a mobile or fixed device such as a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, and a wearable device used by a passenger in the vehicle. The server may be a single server or a server cluster of multiple servers. The electronic device may implement the in-vehicle occupant detection method by way of the processor invoking computer-readable instructions stored in the memory.
Optionally, the electronic device may directly acquire a target image inside the vehicle through the camera device, and detect people inside the vehicle according to the target image. Or the target image in the vehicle can be acquired and sent through the camera devices of other electronic equipment, and the electronic equipment executing the in-vehicle personnel detection method detects the in-vehicle personnel according to the target image after receiving the target image.
The embodiment of the disclosure can be applied to any application scene needing to detect the condition of people in the vehicle, for example, the scene of further detecting the emotional state of infants when the infants exist in the vehicle. Or, further determining a scenario of the infant's safety status when an infant is present in the vehicle.
As shown in fig. 1, the method for detecting an occupant in a vehicle according to the embodiment of the present disclosure may include the following steps S10-30.
And step S10, acquiring a target image of the interior of the vehicle.
In a possible implementation manner, the embodiment of the disclosure can perform in-vehicle personnel detection based on the target image representing the internal condition of the vehicle cabin, that is, the target image needs to be acquired first, and then the in-vehicle personnel detection is performed according to the target image. Alternatively, the target image may include image information of at least one occupant inside the vehicle, for example, including facial image information and body image information of each occupant, so that the identity of each occupant may be further determined based on the facial image information and the body image information. The passenger may be a driver or a common passenger, and the type of the passenger may be one of an adult, a teenager, a child, an infant, and the like. Further, in order to further optimize the occupant identity determination process and further determine the driving safety condition based on the occupant identity after determining the occupant identity, the target image may further include environmental information of an environment in the vehicle where the occupant is located, and may include information of seats and other articles in the vehicle.
Alternatively, the target image may be directly acquired by a camera device built in or connected to the electronic device executing the in-vehicle occupant detection method, or may be acquired by another electronic device in the vehicle and then transmitted to the electronic device executing the in-vehicle occupant detection method. The camera device can be a fixed camera device installed in the vehicle, or other camera devices which are located in the vehicle and can acquire the facial features and body features of all passengers in the vehicle. For example, the image pickup device may be a camera device included in an Occupant Monitoring System (OMS) installed in a vehicle, or a camera device included in an electronic apparatus such as a smartphone carried by an occupant.
Step S20, detecting a child occupant inside the vehicle based on the target image.
In one possible implementation, after a target image of the vehicle interior is acquired, the disclosed embodiments detect a child occupant of the vehicle interior based on the target image. Optionally, the detection manner of the child occupant may be: occupants inside the vehicle are detected based on the target image, and then it is determined whether or not each of the detected occupants is a child occupant. The process of detecting the occupants in the vehicle based on the target image may be face detection and/or body detection based on the target image, obtaining a face detection frame and/or a body detection frame of at least one occupant, and determining whether each occupant is a child occupant based on the face detection frame and/or the body detection frame of each occupant. Further, the detected face detection frame and the detected body detection frame may be analyzed for correlation to determine whether or not both belong to the same human body, thereby correlating the face detection frame and the body detection frame of the same passenger.
Alternatively, human detection may be performed by inputting target images to a neural network that has been trained to identify occupants within the vehicle.
Alternatively, after obtaining the face detection frame and the body detection frame of each passenger, the process of determining whether each passenger is a child passenger may be implemented by training the obtained child recognition model. The face image and/or body image of each occupant is extracted from the target image based on the face detection frame and/or body detection frame of each occupant, for example. And inputting the face image and/or the body image of each passenger into the trained child recognition model to obtain a detection result of whether each passenger is a child passenger. Further, in order to avoid that the recognition result of the model is not ideal or difficult to obtain due to the face or body of the passenger being blocked, it can be determined whether the passenger is visible or not. Namely, for each passenger detected based on the target image, in response to the face image of the passenger meeting the preset face visible condition or the body image of the passenger meeting the preset body visible condition, the face image and/or the body image of the passenger are/is input into the trained child recognition model.
In a possible implementation manner, the face visible condition and the body visible condition may be preset as needed, for example, it may be determined that the face visible condition includes that a ratio of a face occlusion area to a face area does not exceed a first preset value, and the body visible condition includes that a ratio of a body occlusion area to a body area does not exceed a second preset value. Since the body area is small and the number of detail features is large compared to the face, the occluded part of the face may have a large influence on the recognition result. Therefore, the first preset value can be set to be smaller than the second preset value. For example, the face-visible condition is set such that the face-shielded area is not more than 20% and the body-shielded face portion is not more than 33%.
In a possible implementation manner, the process of detecting the child occupant in the vehicle based on the target image may further be that the target image is directly input into a trained child recognition model, when the child recognition model recognizes the target image, the face image and the body image of each occupant are extracted in an image segmentation manner, and then whether the occupant is the child occupant is recognized according to the face image and the body image of each occupant respectively. The child recognition model can identify each passenger according to the body size of the passenger and the key point of the face of the passenger.
Step S30 is to extract a face image of the detected child occupant from the target image.
In one possible implementation manner, when the detection result of the passenger is a child passenger, the region where the face of the child passenger is located is extracted from the target image as a face image, so as to further detect whether the child passenger is an infant passenger again according to the face image. Alternatively, the age range of the child occupant and the age range of the infant occupant may be predetermined, and the age range of the child occupant includes and is greater than the age range of the infant occupant. For example, a child occupant is determined to be an occupant between 0-14 years of age, and an infant occupant is determined to be an occupant between 0-4 years of age.
Step S40, determining whether the child occupant is an infant or not according to the face image of the child occupant.
In a possible implementation manner, after determining that at least one passenger in the target image is a child passenger according to detection by the child passenger, and extracting a face image of the child passenger, further performing finer-grained age detection on the child passenger according to the corresponding face image to determine whether the child passenger includes an infant.
Optionally, the process of further determining whether the child passenger is an infant or not according to the face image of the child passenger may also be implemented by a deep learning model, where the deep learning model may be a binary model for determining whether the category of the person corresponding to the input face image belongs to the infant or not.
Alternatively, the face image of the child occupant may be input to a deep learning model for detecting age, that is, an age detection model, and it is determined whether the age of the child occupant is in an age range corresponding to an infant according to an age detection result of the child occupant output by the age detection model, and if so, the child occupant is determined to be the infant.
Fig. 2 shows a schematic diagram of an in-vehicle occupant detection process according to an embodiment of the present disclosure. As shown in fig. 2, when detecting a person in a vehicle, the embodiment of the present disclosure first acquires a target image 20 including the inside of the vehicle, and extracts facial features and body features 21 of each occupant in the vehicle in the target image. Further, it is determined whether the age of the occupant is within the first age range 22 (i.e., the age range of the child) based on the facial features and the physical features 21 of each occupant. When the age of the occupant is not within the first age range 22, the occupant is determined to be a non-child occupant. When the age of the occupant is within the first age range 22, the occupant is determined to be a child occupant, and the face image 23 of the occupant is further acquired. It is further determined from the face image 23 of the occupant whether the age of the occupant is within a second age range (i.e., the age range of an infant) 24. When the age of the occupant is within the second age range 24, the occupant is determined to be an infant 26. When the age of the occupant is not within the second age range 24, it is determined that the age type of the occupant is a child 25 that is not an infant.
In one possible implementation, the passenger may have a face blocked due to the shooting angle, wearing a mask, and the like, and thus, age identification with finer granularity is difficult. Therefore, in order to reduce the influence of unnecessary identification processes on the detection efficiency of the person in the vehicle, whether the facial features of the passenger in the target image are blocked or not can be determined before judging whether the child passenger is an infant or not. That is, in a case where it is determined that the face image of the child occupant satisfies the preset face visibility condition, it is determined whether the child occupant is an infant or not from the face image of the child occupant. Alternatively, it may be determined that the face image of the child occupant satisfies the preset visibility condition in a case where it is determined from the face image of the child occupant that the ratio of the face blocking area to the face area of the child occupant does not exceed the first preset value.
FIG. 3 shows a schematic diagram of a facial image of a child occupant in accordance with an embodiment of the present disclosure. As shown in fig. 3, after the passenger is determined to be a child passenger, a face image 30 of the passenger in the target image is extracted, and the face of the passenger is analyzed from the face image 30 to find that the face of the passenger has a hair 31 and a mask 32 and other obstructions. Further, the face area of the corresponding occupant of the occupant face image 30, and the areas of the hair 31 and the mask 32 and the like included in the face image 30 can be determined. And obtaining the shielding rate according to the ratio of the area of the shielding object to the area of the face, and determining that the face feature of the passenger in the target image is not shielded under the condition that the shielding rate is not greater than the shielding threshold value. And in the case that the facial features of the passenger in the target image are not blocked, determining whether the passenger is an infant or not according to the facial features of the passenger.
In a possible implementation manner, when it is detected that the vehicle occupant includes an infant according to the vehicle occupant detection method, whether the vehicle infant is safe or not can be further determined according to the vehicle environment and the infant state. For example, in the case where the occupant is an infant, the seat position where the occupant is located may be determined from the target image. And generating prompting seat changing information in response to the seat position where the passenger is located is the front row. That is, when the passenger is an infant and sits on the front row, it is necessary to prompt the passenger to change the seat. The electronic equipment can directly broadcast or display the seat changing prompting information, or send the seat changing prompting information to other electronic equipment for broadcasting or displaying.
Further, in the case where the occupant is an infant, it is also possible to determine whether or not the infant occupant is positioned on the safety seat in the rear row. And in response to that the seat position where the passenger is located is the rear row, determining whether the passenger is located on the safety seat in the rear row according to the target image, and generating safety prompt information under the condition that the passenger is located on the safety seat in the rear row. The safety prompt information can be directly broadcasted or displayed through electronic equipment executing the in-vehicle personnel detection method, or sent to other electronic equipment for broadcasting or displaying, and is used for prompting installation of the safety seat, or placing the infant passenger on the safety seat.
Alternatively, in a scenario where an infant occupant is included in the vehicle, the emotional state of the infant occupant may also be monitored, for example, the emotional state of the infant occupant may be analyzed based on facial features of the infant occupant in the case where the occupant is an infant. In response to the emotional state of the infant occupant being a negative state, placation information is generated. The placation information can be indication information of music playing, and the music can be played through the placation information to placate the infant.
According to the embodiment of the invention, the detection result of the child passenger in the vehicle can be obtained by detecting the target image in the vehicle, and the infant passenger in the vehicle can be detected according to the face image of the child passenger, so that the accurate detection of the infant in the vehicle is realized.
It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.
In addition, the present disclosure also provides an in-vehicle person detection apparatus, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the in-vehicle person detection methods provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are omitted for brevity.
Fig. 4 shows a schematic diagram of an in-vehicle occupant detection apparatus according to an embodiment of the present disclosure, and as shown in fig. 4, the apparatus includes:
an image acquisition module 40 for acquiring a target image of the interior of the vehicle;
a first detection module 41 for detecting a child occupant inside the vehicle based on the target image;
a region extraction module 42 for extracting a face image of the detected child occupant from the target image;
a second detecting module 43, configured to determine whether the child occupant is an infant or not according to the facial image of the child occupant.
In a possible implementation manner, the first detection module 41 includes:
an occupant identification sub-module for detecting an occupant inside the vehicle based on the target image;
and the child passenger judging submodule is used for judging whether the passenger is a child passenger or not aiming at each detected passenger.
In one possible implementation, the occupant identification submodule includes:
the detection frame identification unit is used for carrying out face detection and human body detection based on the target image to obtain a face detection frame and a body detection frame of at least one passenger;
an occupant identification unit for determining whether each occupant is a child occupant based on a face detection frame and a body detection frame of each occupant.
In one possible implementation, the occupant identification unit includes:
a region extraction subunit configured to extract a face image and/or a body image of each occupant from the target image based on a face detection frame and/or a body detection frame of each occupant;
and the model identification subunit is used for inputting the face image and/or the body image of each passenger into the trained child identification model to obtain the detection result of whether each passenger is a child passenger.
In one possible implementation, the model identification subunit includes:
and the condition judging unit is used for responding to that the face image of the passenger meets a preset face visible condition or the body image of the passenger meets a preset body visible condition for each passenger, and inputting the face image and/or the body image of the passenger into the trained child recognition model.
In a possible implementation manner, the face-visible condition includes that a ratio of a face-shielded area to a face area does not exceed a first preset value, and the body-visible condition includes that a ratio of a body-shielded area to a body area does not exceed a second preset value.
In a possible implementation manner, the first preset value is smaller than the second preset value.
In a possible implementation manner, the second detection module 43 includes:
the infant judgment submodule is used for determining whether the child passenger is an infant or not according to the face image of the child passenger under the condition that the face image of the child passenger meets the preset face visible condition.
In one possible implementation, the meeting the preset face visibility condition of the facial image of the child occupant includes:
the ratio of the face shielding area to the face area does not exceed a first preset value.
In one possible implementation, the apparatus further includes:
the position identification module is used for determining the position of a seat where the passenger is located according to the target image under the condition that the passenger is an infant;
and the first information generation module is used for generating prompting seat changing information in response to the situation that the seat where the passenger is located is the front row.
In one possible implementation, the apparatus further includes:
a seat identification module for determining whether the passenger is on a safety seat in the rear row according to the target image when the passenger is an infant;
and the second information generation module is used for generating safety prompt information when the passenger is not positioned on the safety seat at the rear row.
In one possible implementation, the apparatus further includes:
the emotion recognition module is used for determining an emotion state according to facial features of the passenger under the condition that the passenger is an infant;
and the third information generation module is used for responding to the emotional state as a negative state and generating placating information.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a volatile or non-volatile computer readable storage medium.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
The disclosed embodiments also provide a computer program product comprising computer readable code or a non-transitory computer readable storage medium carrying computer readable code, which when run in a processor of an electronic device, the processor in the electronic device performs the above method.
The electronic device may be provided as a terminal, server, or other form of device.
Fig. 5 shows a schematic diagram of an electronic device 800 according to an embodiment of the disclosure. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.
Referring to fig. 5, electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a Complementary Metal Oxide Semiconductor (CMOS) or Charge Coupled Device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as a wireless network (WiFi), a second generation mobile communication technology (2G) or a third generation mobile communication technology (3G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.
Fig. 6 shows a schematic diagram of an electronic device 1900 according to an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 6, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system, such as the Microsoft Server operating system (Windows Server), stored in the memory 1932TM) Apple Inc. of the present application based on the graphic user interface operating System (Mac OS X)TM) Multi-user, multi-process computer operating system (Unix)TM) Free and open native code Unix-like operating System (Linux)TM) Open native code Unix-like operating System (FreeBSD)TM) Or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (15)
1. An in-vehicle occupant detection method comprising:
acquiring a target image of the interior of the vehicle;
detecting a child occupant inside the vehicle based on the target image;
extracting a face image of the detected child occupant from the target image;
determining whether the child occupant is an infant from the facial image of the child occupant.
2. The method of claim 1, wherein the detecting a child occupant within the vehicle interior based on the target image comprises:
detecting an occupant inside the vehicle based on the target image;
for each detected occupant, it is determined whether the occupant is a child occupant.
3. The method of claim 2, wherein the detecting an occupant of the vehicle interior based on the target image comprises:
carrying out face detection and human body detection based on the target image to obtain a face detection frame and a body detection frame of at least one passenger;
whether each occupant is a child occupant is determined based on the face detection frame and the body detection frame of each occupant.
4. The method of claim 3, wherein the determining whether each occupant is a child occupant based on the face detection box and the body detection box of each occupant comprises:
extracting a face image and/or a body image of each passenger from the target image based on a face detection frame and/or a body detection frame of each passenger;
and inputting the face image and/or the body image of each passenger into the trained child recognition model to obtain a detection result of whether each passenger is a child passenger.
5. The method of claim 4, wherein the inputting of the facial image and/or body image of each occupant into the trained child recognition model comprises:
for each passenger, in response to the face image of the passenger meeting a preset face visible condition or the body image of the passenger meeting a preset body visible condition, inputting the face image and/or the body image of the passenger into a trained child recognition model.
6. The method of claim 5, wherein the face-visible condition comprises a ratio of face occlusion area to face area not exceeding a first preset value and the body-visible condition comprises a ratio of body occlusion area to body area not exceeding a second preset value.
7. The method of claim 6, wherein the first preset value is less than the second preset value.
8. The method of any of claims 1-4, wherein the determining whether the child occupant is an infant from the facial image of the child occupant comprises:
and under the condition that the face image of the child passenger is determined to meet the preset face visible condition, determining whether the child passenger is an infant or not according to the face image of the child passenger.
9. The method of claim 8, wherein the child occupant's facial image satisfying a preset facial visibility condition comprises:
the ratio of the face shielding area to the face area does not exceed a first preset value.
10. The method of any of claims 1 to 9, further comprising:
determining the position of a seat where the passenger is located according to the target image when the passenger is an infant;
and generating prompting seat changing information in response to that the seat position where the passenger is located is the front row.
11. The method of claims 1 to 10, further comprising:
determining whether the passenger is on a safety seat in the rear row according to the target image when the passenger is an infant;
and generating safety prompt information when the passenger is not in the safety seat of the rear row.
12. The method of any of claims 1 to 11, further comprising:
determining an emotional state according to facial features of the occupant if the occupant is an infant;
in response to the emotional state being a negative state, generating soothing information.
13. An in-vehicle occupant detection apparatus comprising:
the image acquisition module is used for acquiring a target image in the vehicle;
a first detection module for detecting a child occupant inside the vehicle based on the target image;
the region extraction module is used for extracting a face image of the detected child passenger from the target image;
the second detection module is used for determining whether the child passenger is an infant or not according to the face image of the child passenger.
14. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of claims 1 to 12.
15. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 12.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111272801.XA CN113920492A (en) | 2021-10-29 | 2021-10-29 | Method and device for detecting people in vehicle, electronic equipment and storage medium |
| PCT/CN2022/095587 WO2023071174A1 (en) | 2021-10-29 | 2022-05-27 | Occupancy detection method and apparatus, electronic device, and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111272801.XA CN113920492A (en) | 2021-10-29 | 2021-10-29 | Method and device for detecting people in vehicle, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113920492A true CN113920492A (en) | 2022-01-11 |
Family
ID=79243674
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111272801.XA Pending CN113920492A (en) | 2021-10-29 | 2021-10-29 | Method and device for detecting people in vehicle, electronic equipment and storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113920492A (en) |
| WO (1) | WO2023071174A1 (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114419610A (en) * | 2022-01-26 | 2022-04-29 | 广汽丰田汽车有限公司 | Old man mode switching method and device for vehicle, electronic equipment and storage medium |
| CN115473987A (en) * | 2022-06-20 | 2022-12-13 | 北京罗克维尔斯科技有限公司 | Interaction method, interaction device, electronic equipment and storage medium |
| CN115512315A (en) * | 2022-11-01 | 2022-12-23 | 深圳市城市交通规划设计研究中心股份有限公司 | Non-motor vehicle child riding detection method, electronic device and storage medium |
| WO2023071174A1 (en) * | 2021-10-29 | 2023-05-04 | 上海商汤智能科技有限公司 | Occupancy detection method and apparatus, electronic device, and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101877054A (en) * | 2009-11-23 | 2010-11-03 | 北京中星微电子有限公司 | Method and device for determining age of face image |
| CN103359038A (en) * | 2013-08-05 | 2013-10-23 | 北京汽车股份有限公司 | Method and system for identifying whether child sits at copilot position and automobile |
| CN104627030A (en) * | 2013-11-13 | 2015-05-20 | 光宝科技股份有限公司 | Carrier safety system and safety detecting and processing method for carrier safety system |
| CN106274669A (en) * | 2015-06-10 | 2017-01-04 | 联想(北京)有限公司 | A kind of safe early warning method and device |
| CN111079474A (en) * | 2018-10-19 | 2020-04-28 | 上海商汤智能科技有限公司 | Passenger state analysis method and device, vehicle, electronic device, and storage medium |
| CN111439170A (en) * | 2020-03-30 | 2020-07-24 | 上海商汤临港智能科技有限公司 | Child state detection method and device, electronic equipment and storage medium |
| US20200334453A1 (en) * | 2019-04-16 | 2020-10-22 | GM Global Technology Operations LLC | Vehicle occupant detection |
| CN112507976A (en) * | 2021-01-08 | 2021-03-16 | 蔚来汽车科技(安徽)有限公司 | In-vehicle child protection method, device, computer-readable storage medium and vehicle |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111079479A (en) * | 2018-10-19 | 2020-04-28 | 北京市商汤科技开发有限公司 | Child state analysis method and device, vehicle, electronic device and storage medium |
| KR20200117492A (en) * | 2019-04-04 | 2020-10-14 | 주식회사 만도 | Vehicle control system and vehicle contorl method |
| CN112001348A (en) * | 2020-08-31 | 2020-11-27 | 上海商汤临港智能科技有限公司 | Method and device for detecting passenger in vehicle cabin, electronic device and storage medium |
| CN113920492A (en) * | 2021-10-29 | 2022-01-11 | 上海商汤临港智能科技有限公司 | Method and device for detecting people in vehicle, electronic equipment and storage medium |
-
2021
- 2021-10-29 CN CN202111272801.XA patent/CN113920492A/en active Pending
-
2022
- 2022-05-27 WO PCT/CN2022/095587 patent/WO2023071174A1/en unknown
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101877054A (en) * | 2009-11-23 | 2010-11-03 | 北京中星微电子有限公司 | Method and device for determining age of face image |
| CN103359038A (en) * | 2013-08-05 | 2013-10-23 | 北京汽车股份有限公司 | Method and system for identifying whether child sits at copilot position and automobile |
| CN104627030A (en) * | 2013-11-13 | 2015-05-20 | 光宝科技股份有限公司 | Carrier safety system and safety detecting and processing method for carrier safety system |
| CN106274669A (en) * | 2015-06-10 | 2017-01-04 | 联想(北京)有限公司 | A kind of safe early warning method and device |
| CN111079474A (en) * | 2018-10-19 | 2020-04-28 | 上海商汤智能科技有限公司 | Passenger state analysis method and device, vehicle, electronic device, and storage medium |
| US20200334453A1 (en) * | 2019-04-16 | 2020-10-22 | GM Global Technology Operations LLC | Vehicle occupant detection |
| CN111439170A (en) * | 2020-03-30 | 2020-07-24 | 上海商汤临港智能科技有限公司 | Child state detection method and device, electronic equipment and storage medium |
| CN112507976A (en) * | 2021-01-08 | 2021-03-16 | 蔚来汽车科技(安徽)有限公司 | In-vehicle child protection method, device, computer-readable storage medium and vehicle |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023071174A1 (en) * | 2021-10-29 | 2023-05-04 | 上海商汤智能科技有限公司 | Occupancy detection method and apparatus, electronic device, and storage medium |
| CN114419610A (en) * | 2022-01-26 | 2022-04-29 | 广汽丰田汽车有限公司 | Old man mode switching method and device for vehicle, electronic equipment and storage medium |
| CN115473987A (en) * | 2022-06-20 | 2022-12-13 | 北京罗克维尔斯科技有限公司 | Interaction method, interaction device, electronic equipment and storage medium |
| CN115512315A (en) * | 2022-11-01 | 2022-12-23 | 深圳市城市交通规划设计研究中心股份有限公司 | Non-motor vehicle child riding detection method, electronic device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2023071174A1 (en) | 2023-05-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3133528B1 (en) | Method and apparatus for fingerprint identification | |
| CN113920492A (en) | Method and device for detecting people in vehicle, electronic equipment and storage medium | |
| US20210133468A1 (en) | Action Recognition Method, Electronic Device, and Storage Medium | |
| CN107692997B (en) | Heart rate detection method and device | |
| CN112669583B (en) | Alarm threshold adjusting method and device, electronic equipment and storage medium | |
| EP2911126A1 (en) | Method and device for protecting terminal apparatus and terminal apparatus and medium | |
| US20170123587A1 (en) | Method and device for preventing accidental touch of terminal with touch screen | |
| CN110569777B (en) | Image processing method and device, electronic device and storage medium | |
| US9924090B2 (en) | Method and device for acquiring iris image | |
| CN113486760A (en) | Object speaking detection method and device, electronic equipment and storage medium | |
| CN112001348A (en) | Method and device for detecting passenger in vehicle cabin, electronic device and storage medium | |
| CN108121631B (en) | Screen abnormal state reminding method and device | |
| CN111435422B (en) | Action recognition method, control method and device, electronic equipment and storage medium | |
| CN110909203A (en) | Video analysis method and device, electronic equipment and storage medium | |
| CN112270288A (en) | Living body identification method, access control device control method, living body identification device, access control device and electronic device | |
| CN114882681A (en) | Work order processing method and device, electronic equipment and storage medium | |
| CN113011291A (en) | Event detection method and device, electronic equipment and storage medium | |
| CN112667984A (en) | Identity authentication method and device, electronic equipment and storage medium | |
| CN113486759B (en) | Dangerous action recognition method and device, electronic equipment and storage medium | |
| CN113488043B (en) | Passenger speaking detection method and device, electronic equipment and storage medium | |
| CN113989889A (en) | Shading plate adjusting method and device, electronic equipment and storage medium | |
| CN110543928B (en) | Method and device for detecting number of people on trackless rubber-tyred vehicle | |
| CN107133551B (en) | Fingerprint verification method and device | |
| WO2023071175A1 (en) | Method and apparatus for associating person with object in vehicle, and electronic device and storage medium | |
| CN110826045B (en) | Authentication method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| REG | Reference to a national code |
Ref country code: HK Ref legal event code: DE Ref document number: 40064937 Country of ref document: HK |