CN109389791B - A detect object and be close early warning system for vehicle - Google Patents
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/19—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using infrared-radiation detection systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19695—Arrangements wherein non-video detectors start video recording or forwarding but do not generate an alarm themselves
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
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Abstract
The invention provides a detected object approach early warning system for a vehicle, when a person or an object approaches the vehicle within 50 centimeters and stays for more than 3 seconds, an intelligent warning system of the vehicle detects the staying person or object through a radar, then an infrared detection system and a dynamic detection system are started, a software system starts to evaluate the possible risk of theft or damage of the vehicle through the detection and scanning of the person or object outside the vehicle, simultaneously an image recording function outside the vehicle or inside the vehicle is started, warning data is sent to a warning processing center or a mobile terminal of a vehicle user through a communication signal, the scene can be monitored in real time to make correct judgment, and sound and light warning and voice warning can be started if necessary. The invention has the advantages that: the active early warning system can react when people or objects approach, precaution is achieved in advance, timely treatment is achieved, and damage or theft of vehicles is effectively reduced.
Description
Technical Field
The invention belongs to the technical field of automobile theft prevention, and particularly relates to an object proximity detection early warning system for a vehicle.
Background
China is a large motor vehicle country, motor vehicles are reserved in the front of the world, but most vehicles do not have garages, the vehicle owners often stop the vehicles in the open air sections at two sides of a community road, parking safety becomes a concern of the vehicle owners, the vehicles stopped in the open air sections at two sides of the community road are often damaged, but the vehicles cannot find troublemishers due to the fact that no monitoring equipment exists, great troubles are brought to the vehicle owners, and social harmony is not facilitated.
In the prior patent documents, patent publication nos.: CN1537756A discloses: an antitheft device 91 for preventing vehicle theft includes a determination device for determining whether or not an approaching object T exists in a vehicle M based on information obtained from a proximity monitoring sensor 93 for monitoring the vicinity of the vehicle, a determination device for determining whether or not the approaching object T of the vehicle M is a suspect based on information obtained from an operation sensor 94 for monitoring the periphery and/or interior of the vehicle M when it is determined that the approaching object T is a suspect, and a device for performing processing such as lighting an indicator 38 when it is determined that the approaching object T of the vehicle M is a suspect. Thus, the purpose of providing the vehicle anti-theft device capable of improving the detection precision is achieved.
However, such a monitoring device must monitor the whole process in real time, and has no communication function, which brings great inconvenience to the use. In addition, at present, the number of motor vehicles in China is large, many motor vehicles do not have fixed parking spaces, many vehicles are parked in places with dense people flows, many people come and go, and the situations are very complicated. For the situations of a passerby and the like who occasionally stands beside a vehicle for chatting, the technology of the patent often generates false alarm, troubles are generated for the vehicle owner, and time and energy of the vehicle owner are wasted.
Disclosure of Invention
The purpose of the invention is realized by the following technical scheme.
A detected object proximity warning system for a vehicle, comprising:
the radar detection sensor is used for detecting objects around the vehicle in real time, judging whether the object is further close to the vehicle or not according to the invasion distance of the object when the intrusion of a foreign object into a preset boundary of a detection area is detected, and triggering and starting the dynamic detection sensor if the invaded object is further close to the vehicle and keeps staying for more than a preset time;
the dynamic detection sensor is used for detecting the action of an intruding object, judging whether the object has further damage behavior, and triggering and starting the infrared detection sensor if the object is detected to continuously approach and keep a non-static state;
the system comprises an infrared detection sensor and a monitoring camera, wherein the infrared detection sensor detects the position of an invaded object, and the monitoring camera records an image;
the vehicle-mounted computer control processing center is used for calculating the graph and the dynamic track of the invaded object according to the position and the image of the invaded object, judging whether the vehicle is invaded and damaged, and triggering and starting the information alarm processor;
and the information alarm processor is used for sending a result of judging whether the vehicle is invaded and damaged to the cloud big data processing center or the vehicle owner mobile terminal.
Preferably, the preset boundary is within 50 cm.
Preferably, the preset time is 3 seconds.
Preferably, the information alarm processor further sends the image of the invading object, the invading time and place and the invading time period to the cloud big data processing center, and stores the image as an event record file.
Preferably, the vehicle owner sends an instruction to the vehicle-mounted computer control processing center according to the character and graphic information received by the mobile terminal, wherein the instruction comprises the steps of canceling an alarm, sending a sound and light alarm, giving a language alarm for driving away and/or giving an alarm to the police.
Preferably, the vehicle-mounted computer control processing center firstly judges whether the person is the other object or not according to the graph and the dynamic track of the invaded object, if the person is the other object, the alarm is not given, and if the person is the person, the portrait comparison recognition is started, and whether the person is the vehicle owner or the allowed family member or friend is recognized.
Preferably, the portrait contrast identification includes the following steps:
(1) extracting face image information;
(2) learning the face image information by using a deep learning model;
(3) comparing the face image information with face image information stored in an identity database, and performing depth matching by using a binary tree algorithm;
(4) and when the matching result exceeds a set threshold value, determining that the face matching result is successful, otherwise, failing to match.
Preferably, the deep learning model is an LSTM network model, the LSTM network model is a two-layer LSTM model connected in series, and the two-layer LSTM model is located in a hidden layer.
Preferably, the text and graphic information received by the mobile terminal is encrypted data, wherein the used encryption algorithm comprises: secure hash algorithm, MD5 algorithm.
Preferably, the secure hash algorithm is used for processing message data, and comprises the following steps:
(1) padding the additional bits, the padding message to have a length ≡ 896;
(2) an additional length, which is a 128-bit block appended after the padded message, considered as an unsigned integer, which contains the length of the previous message;
(3) initializing a Hash buffer, storing the intermediate result and the final result of the Hash function in a 512-bit buffer, wherein the buffer is represented by 8 64-bit registers, and initializing the registers to 64-bit integers;
(4) and processing the message by using a 1024-bit packet as a unit and outputting a result.
The mode of acquiring the content of each register is as follows: the first 8 prime numbers are taken to take the square root and the first 64 bits of the fractional part.
Preferably, the MD5 is configured to process information data, and includes the following steps:
(1) padding, namely padding information to ensure that the result of the remainder of the bit length to 512 is equal to 448;
(2) initializing variables, wherein the initial 128-bit values are four initial linking variables, and the parameters are used for the first round of operation and are expressed in big-endian;
(3) the algorithm flow for processing packet data, each packet, is as follows:
the first group copies the four link variables into other four variables, and the variables from the second group are the operation results of the previous group;
the main loop has four rounds, the first round carries out 16 times of operations, each time of operation carries out one time of nonlinear function operation on three of the other four variables, and then the obtained result is added with a fourth variable, a sub-group of the text and a constant; the result is then shifted a variable number to the left and one of the four other variables is added and finally the result is substituted for one of the four other variables.
(4) The output, the final output, is a cascade of the other four variables.
Preferably, the method for filling information in step (1) is as follows:
1) filling a 1 and an infinite number of 0's behind the information until the above condition is satisfied, stopping the filling of the information with 0's;
2) this result is followed by a length of information before stuffing in 64-bit binary representation, which is taken as the lower 64 bits if the length of information before stuffing in binary representation exceeds 64 bits.
The invention has the advantages that: the active early warning system can react when people or objects approach, precaution is achieved in advance, timely treatment is achieved, and damage or theft of vehicles is effectively reduced.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 illustrates a vehicle exterior view according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a system for detecting an object approaching an early warning according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The invention provides an early warning system for automatically starting a vehicle to detect the approach of people or objects after the vehicle is parked or locked.
The principle of the invention is as follows: as shown in fig. 1, for example, when a person or an object approaches the vehicle within 50 centimeters and stays for more than 3 seconds, after the intelligent warning system of the vehicle detects the person or the object staying through the radar, the infrared detection system and the dynamic detection system are started, the software system starts to evaluate the possible risk of theft or damage of the vehicle through the detection and scanning of the person or the object outside the vehicle, and simultaneously starts the image recording function outside the vehicle or inside the vehicle, and sends the warning data to the warning processing center or the mobile terminal of the vehicle user through the communication signal, so that the scene can be monitored in real time to make correct judgment, and sound and light alarms and voice warnings can be started if necessary.
Example 1
As shown in fig. 2, the detected object proximity warning system for a vehicle of the present invention includes:
the vehicle-mounted computer control processing center: after the vehicle is parked and the vehicle owner is flamed out and gets off the vehicle and locks the vehicle, the system starts the monitoring system. The vehicle-mounted computer control processing center is positioned in the central control of the vehicle and can be realized by combining a computer (or other hardware control modules such as a single chip microcomputer, an FPGA and the like) with a corresponding software control system. The radar monitoring, the dynamic monitoring and the infrared monitoring can be started or stopped by the vehicle-mounted computer control processing center, and the information alarm is controlled to be started in emergency or when the vehicle is invaded. Radar monitoring, motion monitoring, infrared detection and information alarm are a sequential process, and are started in sequence as shown in fig. 2. The specific process is as detailed below.
Radar detection sensor: and (3) real-time detection, wherein once a foreign object is detected to invade the boundary of the detection area of 50 cm, whether the vehicle is further approached is judged according to the cm distance invaded by the object, if the invaded object is further approached and stays, the radar sensor sends a signal to the computer after three seconds, and the computer starts the dynamic detection sensor.
The dynamic detection sensor: the method comprises the steps of waiting in real time, detecting the action of an intruding object after receiving a computer instruction, preliminarily judging whether the object has a further damage behavior, sending suggestion information to a computer by a dynamic detection sensor if the object continuously approaches and keeps a non-static state, and sending an instruction to start an infrared detection sensor by the computer.
Infrared detection sensor and surveillance camera head: the infrared detection sensor starts to detect the position of the locked intruding object, the monitoring camera records images, the size and the action of the object can be judged and read according to the shape of the object, the vehicle-mounted computer control processing center is used for calculating the graph and the dynamic track of the intruding object, whether the vehicle is intruded or damaged is judged, and the information alarm processor is started regardless of whether the vehicle is damaged or not.
An information alarm processor: after judging that the intruding object has no further damage behavior or intention, or judging that the intruding object has no further damage behavior or intention, starting the wireless communication module, and sending corresponding information to the vehicle owner through wireless communication signals of a wireless local area network, a telecommunication network and the like. Meanwhile, the alarm processor sends the images of the invaded objects, the invaded time and place and the invaded time period to the cloud big data processing center, and the images are stored as event record files. The vehicle owner sends a next processing instruction to the vehicle-mounted computer through the vehicle owner terminal according to the received character and graphic information: including canceling the alarm, sounding and lighting an audible and visual alarm, warning with a language to drive away, alerting the police, etc.
In the invention, the vehicle-mounted computer control processing center firstly judges whether the person is a person or other objects (such as animals, other vehicles and the like) according to the graph and the dynamic track of the invaded object, does not give an alarm if the person is the other objects, and starts portrait comparison and identification if the person is the person to identify whether the person is a car owner or an allowed family member or friend.
The method for identifying the suspicious personnel through the portrait contrast identification algorithm comprises the following steps:
(1) extracting face image information;
(2) learning the face image information by using a deep learning model;
(3) comparing the face image information with face image information stored in an identity database, and performing depth matching by using a binary tree algorithm;
(4) and when the matching result exceeds a set threshold value, the face matching result is determined to be successful, otherwise, the matching is failed, and the suspicious person is a person with high theft risk.
The deep learning model is an LSTM network model, the LSTM network model is a two-layer LSTM model which is connected in series, and the two-layer LSTM model is located in a hidden layer.
In the invention, the corresponding information sent to the owner is encrypted data so as to prevent the encrypted data from being easily cracked after being intercepted by a thief. The encryption algorithm used comprises: secure hash algorithm, MD5 algorithm.
The secure hash algorithm is used for processing message data and comprises the following steps:
(1) padding the additional bits, the padding message to have a length ≡ 896;
(2) an additional length, which is a 128-bit block appended after the padded message, considered as an unsigned integer, which contains the length of the previous message;
(3) initializing a Hash buffer, storing the intermediate result and the final result of the Hash function in a 512-bit buffer, wherein the buffer is represented by 8 64-bit registers, and initializing the registers to 64-bit integers;
(4) and processing the message by using a 1024-bit packet as a unit and outputting a result.
The mode of acquiring the content of each register is as follows: the first 8 prime numbers are taken to take the square root and the first 64 bits of the fractional part.
The MD5 is used for processing information data, and comprises the following steps:
(1) padding, namely padding information to ensure that the result of the remainder of the bit length to 512 is equal to 448;
(2) initializing variables, wherein the initial 128-bit values are four initial linking variables, and the parameters are used for the first round of operation and are expressed in big-endian;
(3) the algorithm flow for processing packet data, each packet, is as follows:
the first group copies the four link variables into other four variables, and the variables from the second group are the operation results of the previous group;
the main loop has four rounds, the first round carries out 16 times of operations, each time of operation carries out one time of nonlinear function operation on three of the other four variables, and then the obtained result is added with a fourth variable, a sub-group of the text and a constant; the result is then shifted a variable number to the left and one of the four other variables is added and finally the result is substituted for one of the four other variables.
(4) The output, the final output, is a cascade of the other four variables.
The method for filling information in the step (1) is as follows:
1) filling a 1 and an infinite number of 0's behind the information until the above condition is satisfied, stopping the filling of the information with 0's;
2) this result is followed by a length of information before stuffing in 64-bit binary representation, which is taken as the lower 64 bits if the length of information before stuffing in binary representation exceeds 64 bits.
At present, most domestic vehicles send signals after receiving vibration, and the active early warning system can respond when people or objects approach, so that precaution is realized in advance, timely treatment is realized, and damage or theft of the vehicles is effectively reduced.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (5)
1. A detected object proximity warning system for a vehicle, comprising:
the radar detection sensor is used for detecting objects around the vehicle in real time, judging whether the object is further close to the vehicle or not according to the invasion distance of the object when the intrusion of a foreign object into a preset boundary of a detection area is detected, and triggering and starting the dynamic detection sensor if the invaded object is further close to the vehicle and keeps staying for more than a preset time;
the dynamic detection sensor is used for detecting the action of an intruding object, judging whether the object has further damage behavior, and triggering and starting the infrared detection sensor if the object is detected to continuously approach and keep a non-static state;
the system comprises an infrared detection sensor and a monitoring camera, wherein the infrared detection sensor detects the position of an invaded object, and the monitoring camera records an image;
the vehicle-mounted computer control processing center is used for calculating the graph and the dynamic track of the invaded object according to the position and the image of the invaded object, judging whether the vehicle is invaded and damaged, and triggering and starting the information alarm processor;
the information alarm processor is used for sending a result of judging whether the vehicle is invaded and damaged to the cloud big data processing center or the vehicle owner mobile terminal;
the vehicle-mounted computer control processing center firstly judges whether a person or other objects exist according to the figures and dynamic tracks of the invaded objects, if the person exists, the vehicle-mounted computer control processing center does not give an alarm, if the person exists, the vehicle-mounted computer control processing center starts portrait comparison and identification, and whether the person exists a vehicle owner or an allowed family member or friend is identified; the portrait comparison and identification method comprises the following steps:
(1) extracting face image information;
(2) learning the face image information by using a deep learning model;
(3) comparing the face image information with face image information stored in an identity database, and performing depth matching by using a binary tree algorithm;
(4) when the matching result exceeds a set threshold value, the face matching result is determined to be successful, otherwise, the matching is failed;
the deep learning model is an LSTM network model, the LSTM network model is a two-layer LSTM model connected in series, and the two-layer LSTM model is positioned on a hidden layer;
the text and graphic information received by the mobile terminal is encrypted data, wherein the used encryption algorithm comprises the following steps: a secure hash algorithm, MD5 algorithm;
the secure hash algorithm is used for processing message data and comprises the following steps:
(1) padding the additional bits, the padding message to have a length ≡ 896;
(2) an additional length, which is a 128-bit block appended after the padded message, considered as an unsigned integer, which contains the length of the previous message;
(3) initializing a Hash buffer, storing the intermediate result and the final result of the Hash function in a 512-bit buffer, wherein the buffer is represented by 8 64-bit registers, and initializing the registers to 64-bit integers;
(4) processing the message by using 1024-bit grouping as a unit and outputting a result;
the mode of acquiring the content of each register is as follows: taking the first 8 prime numbers to take the square root and the first 64 bits of the decimal part;
the MD5 is used for processing information data, and comprises the following steps:
(1) padding, namely padding information to ensure that the result of the remainder of the bit length to 512 is equal to 448;
(2) initializing variables, wherein the initial 128-bit values are four initial linking variables, and the parameters are used for the first round of operation and are expressed in big-endian;
(3) the algorithm flow for processing packet data, each packet, is as follows:
the first group copies the four initial test link variables into other four variables, and the variables from the second group are the operation results of the previous group;
the main loop has four rounds, the first round carries out 16 times of operations, each time of operation carries out one time of nonlinear function operation on three of the other four variables, and then the obtained result is added with a fourth variable, a sub-group of the text and a constant; shifting the result by an indefinite number to the left, adding one of the other four variables, and finally replacing one of the other four variables with the result;
(4) the final output is the cascade of the other four variables;
the method for filling information in the step (1) is as follows:
1) filling a 1 and an infinite number of 0's behind the information until the above condition is satisfied, stopping the filling of the information with 0's;
2) this result is followed by a length of information before stuffing in 64-bit binary representation, which is taken as the lower 64 bits if the length of information before stuffing in binary representation exceeds 64 bits.
2. The detected object proximity warning system for a vehicle as claimed in claim 1, wherein:
the preset boundary is within 50 centimeters.
3. The detected object proximity warning system for a vehicle as claimed in claim 1, wherein:
the preset time is 3 seconds.
4. The detected object proximity warning system for a vehicle as claimed in claim 1, wherein:
and the information alarm processor also sends the images of the invaded objects, the invaded time and place and the time period to the cloud big data processing center to be stored as an event record file.
5. The detected object proximity warning system for a vehicle as claimed in claim 1, wherein:
the vehicle owner sends instructions to the vehicle-mounted computer control processing center according to the character and graphic information received by the mobile terminal, wherein the instructions comprise canceling an alarm, sending a sound and light alarm, warning with a language to drive away and/or alarming to police.
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| CN201811063620.4A CN109389791B (en) | 2018-09-12 | 2018-09-12 | A detect object and be close early warning system for vehicle |
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| CN201811063620.4A CN109389791B (en) | 2018-09-12 | 2018-09-12 | A detect object and be close early warning system for vehicle |
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| CN109389791B true CN109389791B (en) | 2021-06-15 |
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| CN111046822A (en) * | 2019-12-19 | 2020-04-21 | 山东财经大学 | Large vehicle anti-theft method based on artificial intelligence video identification |
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