CN108694388B - Campus monitoring method and device based on intelligent camera - Google Patents
Campus monitoring method and device based on intelligent camera Download PDFInfo
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- CN108694388B CN108694388B CN201810460108.7A CN201810460108A CN108694388B CN 108694388 B CN108694388 B CN 108694388B CN 201810460108 A CN201810460108 A CN 201810460108A CN 108694388 B CN108694388 B CN 108694388B
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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
- G06V40/172—Classification, e.g. identification
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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- 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
- G06V40/168—Feature extraction; Face representation
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Abstract
The invention provides a campus monitoring method based on an intelligent camera and corresponding equipment, wherein the method comprises the steps of obtaining monitoring data in a preset area and extracting preset object data in the monitoring data; classifying the preset object data to obtain a plurality of types of object data, and calculating the data proportion of each type of object data in the preset object data; and judging that the data proportion exceeds a preset data proportion value, and generating early warning information. According to the campus monitoring method and device based on the intelligent camera, provided by the invention, the information identification of the monitored image data is carried out, the event data occurring in the monitored image data is compared with the preset event conditions, the occurrence of the preset event is automatically found, the corresponding alarm is triggered, and the possibility of timely realizing the processing of the corresponding event by related personnel is provided.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring, in particular to a campus monitoring method and device based on an intelligent camera.
Background
The campus is a cultural education place for learning and improvement, and should be quiet and peaceful, however, violent events in the campus are not even news, and the maintenance of campus security is a task which needs to be adhered to for a long time in campus management of college schools. Generally, there are three requirements in terms of campus security: personnel, property safety and emergency treatment requirements of the teaching environment; location service, attendance requirements from teachers; student path tracking from parents, location services, and alarm services, among others. Monitoring of teaching environments and even campus environments is an important measure in the aspect of campus security assurance, however, various security monitoring technologies exist in the prior art, the functions are single, the pertinence is poor, only images can be recorded simply, the image contents cannot be understood, corresponding reactions cannot be automatically made for the image contents, only a little convenience can be provided for later evidence extraction, and the method is useless for stopping campus violence events.
Disclosure of Invention
In view of the above, it is desirable to provide a campus monitoring method and apparatus based on an intelligent camera, which address at least one of the above-mentioned problems.
A campus monitoring method based on an intelligent camera is suitable for being executed in computing equipment and comprises the following steps:
acquiring monitoring data in a preset area, and extracting preset object data in the monitoring data;
classifying the preset object data to obtain a plurality of types of object data, and calculating the data proportion of each type of object data in the preset object data;
and judging that the data proportion exceeds a preset data proportion value, and generating early warning information.
The invention correspondingly provides a campus monitoring device based on an intelligent camera, which comprises:
the acquisition module is used for acquiring monitoring data in a preset area and extracting preset object data in the monitoring data;
the calculation module is used for classifying the preset object data, acquiring a plurality of types of object data and calculating the data proportion of each type of object data in the preset object data;
and the early warning module is used for judging that the data proportion exceeds a preset data proportion value and generating early warning information.
Meanwhile, the invention also provides campus monitoring equipment based on the intelligent camera, which comprises a processor and a memory for storing executable instructions of the processor; wherein the processor is configured to:
acquiring monitoring data in a preset area, and extracting preset object data in the monitoring data;
classifying the preset object data to obtain a plurality of types of object data, and calculating the data proportion of each type of object data in the preset object data;
and judging that the data proportion exceeds a preset data proportion value, and generating early warning information.
According to the campus monitoring method and device based on the intelligent camera, provided by the invention, the information identification of the monitored image data is carried out, the event data occurring in the monitored image data is compared with the preset event conditions, the occurrence of the preset event is automatically found, the corresponding alarm is triggered, and the possibility of timely realizing the processing of the corresponding event by related personnel is provided.
Drawings
Fig. 1 is a flowchart of a campus monitoring method based on an intelligent camera according to an embodiment of the present invention;
fig. 2 is a block diagram of a campus monitoring device based on an intelligent camera according to an embodiment of the present invention.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
A campus monitoring method based on an intelligent camera is suitable for being executed in computing equipment, and as shown in FIG. 1, the campus monitoring method comprises steps S100 to S300:
step S100: and acquiring monitoring data in a preset area, and extracting preset object data in the monitoring data. The monitoring equipment collects video stream data collected by a certain intelligent camera, the video stream data comprises frame images collected according to a certain frequency within a certain time, and continuous playing can be carried out at a certain playing speed. The monitoring data includes both image data and sound data, and correspond to each other according to the same time stamp. And analyzing the monitoring data according to a computer image recognition technology, and acquiring the number and/or positions of the preset objects with the pre-stored characteristic information in the monitoring data to form preset object data. And identifying predetermined object data from the monitoring data, wherein the predetermined object data comprises data corresponding to personnel, animals, vehicles, still life and the like, counting the number, the geographic position, the position relation between the predetermined objects and the like of the predetermined objects, and taking the data as the predetermined object data and providing preparation for further processing. The related art is capable of knowing specific computer recognition techniques according to the prior art, and will not be described herein.
Step S200: classifying the preset object data to obtain a plurality of types of object data, and calculating the data proportion of each type of object data in the preset object data. After the monitoring device acquires and distinguishes the predetermined object data, further classifying the data according to a computer identification technology, specifically, classifying the predetermined object data to acquire a plurality of classes of object data includes: and comparing the preset object data with a plurality of pre-stored object data, and judging and distinguishing the class object data matched with the pre-stored object data. The predetermined object data can be classified by comparing the predetermined object data with related data stored in a monitoring system or a cloud server, for example, in campus monitoring, the personnel data are classified, the identities of the personnel data are recognized, campus workers, students, teachers, non-school personnel and the like are divided, and information data of the campus workers, students, teachers and the like serving as the pre-stored object data can be pre-recorded, for example, image recording during admission or work entering. Preferably, the concave-convex lens image increasing algorithm is adopted to process the monitoring data to obtain processed monitoring data, and the face information feature extraction is carried out on the processed monitoring data to obtain the face information data. Capturing a campus image in front of the camera by the high-frequency camera, increasing one image to be multiple by a concave-convex lens image increasing algorithm, and selecting the clearest image with the best quality from the images to process the image. The image is preprocessed firstly, face information is corrected, face alignment is carried out, face information features are extracted, and the face information which is as complete as possible is obtained. The image increasing algorithm uses the convex lens imaging principle to obtain more images by changing the size of a spectrogram. Furthermore, the face information data is processed according to the ridgelet transform and wavelet threshold function algorithm, and the processed face information data is obtained. The denoising process of the algorithm is the fusion of wavelet threshold denoising and ridgelet transformation, and comprises the following specific steps:
(1) carrying out wavelet decomposition on the noisy face information and denoising by using an improved wavelet threshold number;
(2) reconstructing an original image by using the denoised wavelet coefficient;
(3) carrying out Radon transformation on the noisy face information and carrying out wavelet transformation on a Radon matrix obtained by transformation;
(4) denoising a matrix obtained by wavelet transformation by using an improved wavelet threshold function;
(5) performing wavelet inverse transformation on the matrix subjected to denoising;
(6) performing Radon inverse transformation on the matrix obtained by wavelet inverse transformation;
(7) and fusing the image obtained by denoising the wavelet threshold and the image obtained by denoising the ridge wave.
The ridgelet transform denoising method has good performance on line singularity, the wavelet threshold function denoising method has good performance on point singularity, and for a graph, the two methods are combined to achieve better image denoising and obtain more accurate face information data.
Extracting characteristic data in the processed human face information data, wherein the characteristic data comprises human eye contour shape, relative positions of facial organs and the like, comparing the characteristic data with characteristic data in pre-stored object data, and judging and distinguishing class object data matched with the pre-stored object data.
After distinguishing the class object data, the total number of the predetermined object data and the number of the object data of each class in the image data can be simultaneously obtained, so that the computer can calculate the proportion of the object data of each class in the image data, that is, the data proportion of the class object data in the predetermined object data, wherein the data proportion refers to the proportion of a certain item of data in the total data, for example, when 20 students, 5 trees and 3 houses appear in an image taken at a certain campus at a special moment, the proportion of the object data as students in the predetermined object data is 20/38.
Step S300: and judging that the data proportion exceeds a preset data proportion value, and generating early warning information. The preset data and specific gravity values are also prestored in the system database and are called for comparison when needed. In the case mentioned above, when the data proportion 20/38 of the category object data is obtained, and the number of students usually appearing in the image of the campus is only 10 at ordinary times, the proportion is 10/38, that is, the preset data proportion value is 25%, it indicates that there is an abnormal event when the students gather, or when there is an abnormal person in the students, the data proportion exceeds 0 for the abnormal person in the campus, and it also indicates that there is an abnormal event, a corresponding warning signal is sent to remind the relevant person to pay attention to the situation. Preferably, the specific content of the data proportion exceeding the preset data proportion value is judged not only to mean single data comparison, but also the number of the data proportion exceeding the preset data proportion value of a plurality of items of class object data reaches a preset threshold value, for example, three items of data proportion of thought class data of students, workers, teachers and non-school personnel exceed respective preset data proportion values at the same time, and the preset threshold value is 2, the system generates early warning information, and a more accurate early warning signal is obtained.
As a preferable scheme, as described above, the monitoring data further includes sound information data, that is, the monitoring camera can record not only images but also take in sound, and accordingly, the predetermined object data and the category object data also include sound information data, and the system simultaneously determines that the sound information data in each category object data conforms to the preset alarm sound data, and generates the warning information. Preferably, the specific scheme is that the loudness data in the sound information data is judged to reach a loudness threshold value in preset alarm sound data, alarm information is generated, for example, when it is detected that students yell with loud voice in images or videos and the sound decibel number exceeds a set sound decibel number, it is indicated that an abnormal event may exist, and early warning information is sent. Of course, the position information of the predetermined object may also be used to determine whether a special event occurs, for example, when the computer recognition system determines that the position information included in the two or more types of object data reaches the preset position information and exceeds the preset time threshold or number threshold, it is reflected that the special event may exist at the position, and an early warning message needs to be sent out to remind the user.
As a preferable scheme, after the step of generating the warning information, the step of sending the warning information to the setting terminal device is further included. For example, an alarm sound is given out through an alarm device, or a reminding message is given out to a wireless communication device of related personnel through a wireless network, or an alarm channel directly connected with a public security institution gives out alarm information.
Accordingly, the present invention provides a campus monitoring device based on an intelligent camera, as shown in fig. 2, including:
the obtaining module 10 is configured to obtain monitoring data in a predetermined area, and extract predetermined object data in the monitoring data. The calculating module 20 is configured to classify the predetermined object data, obtain a plurality of types of object data, and calculate a data proportion of each type of object data in the predetermined object data. And the early warning module 30 is used for judging that the proportion of the data exceeds a preset data proportion value and generating early warning information.
Meanwhile, based on the idea of a computer system, the invention accordingly provides an intelligent camera-based monitoring device, which comprises a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the following steps: acquiring monitoring data in a preset area, and extracting preset object data in the monitoring data; classifying the preset object data to obtain a plurality of types of object data, and calculating the data proportion of each type of object data in the preset object data; and judging that the data proportion exceeds a preset data proportion value, and generating early warning information.
The apparatuses or modules illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer, which may take the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email messaging device, game console, tablet computer, wearable device, or a combination of any of these devices.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. Especially, for the server device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for relevant points, refer to part of the description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The above description is only exemplary of the preferred embodiment of one or more embodiments of the present disclosure, and is not intended to limit the present disclosure, so that any modification, equivalent replacement, or improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (8)
1. A campus monitoring method based on an intelligent camera is suitable for being executed in computing equipment, and is characterized by comprising the following steps:
acquiring monitoring data in a predetermined area;
processing the monitoring data by adopting a concave-convex lens image increasing algorithm to obtain processed monitoring data, and extracting the face information characteristics of the processed monitoring data to obtain face information data;
processing the face information data according to a ridgelet transform and wavelet threshold function algorithm to obtain the processed face information data, wherein the processing comprises the following steps:
(1) carrying out wavelet decomposition on the noisy face information and denoising by using an improved wavelet threshold number;
(2) reconstructing an original image by using the denoised wavelet coefficient;
(3) carrying out Radon transformation on the noisy face information and carrying out wavelet transformation on a Radon matrix obtained by transformation;
(4) denoising a matrix obtained by wavelet transformation by using an improved wavelet threshold function;
(5) performing wavelet inverse transformation on the matrix subjected to denoising;
(6) performing Radon inverse transformation on the matrix obtained by the wavelet inverse transformation;
(7) fusing an image obtained by denoising the wavelet threshold and an image obtained by denoising the ridge wave;
extracting characteristic data in the processed face information data, comparing the characteristic data with characteristic data in prestored object data, and judging and distinguishing class object data matched with the prestored object data;
calculating the data proportion of each category of object data in the pre-stored object data;
and judging that the data proportion exceeds a preset data proportion value, and generating early warning information.
2. The campus monitoring method based on intelligent cameras as claimed in claim 1, wherein the step of acquiring the monitoring data in a predetermined area and extracting the pre-stored object data in the monitoring data specifically comprises:
and analyzing the monitoring data according to a computer image recognition technology, and acquiring the number and/or the position of pre-stored objects with pre-stored characteristic information in the monitoring data to form the pre-stored object data.
3. The intelligent-camera-based campus monitoring method according to claim 1, wherein the step of classifying the pre-stored object data and acquiring a plurality of classes of object data specifically comprises:
and comparing the pre-stored object data with a plurality of pre-stored object data, and judging and distinguishing the class object data matched with the pre-stored object data.
4. The campus monitoring method based on intelligent cameras as claimed in claim 1, wherein the monitoring data, the pre-stored object data and the class object data further comprise sound information data, and it is determined that the sound information data in each class object data conforms to preset alarm sound data to generate early warning information.
5. The smart-camera-based campus monitoring method according to claim 4, wherein the step of determining that the sound information data in the object data of each category conforms to preset alarm sound data, and the step of generating the warning information specifically comprises determining that loudness data in the sound information data reaches a loudness threshold value in the preset alarm sound data, and generating the warning information.
6. The intelligent-camera-based campus monitoring method according to claim 1, wherein after the step of generating the warning information, the method further comprises sending the warning information to a set terminal device.
7. The utility model provides a campus monitoring device based on intelligent camera which characterized in that includes:
the acquisition module is used for acquiring monitoring data in a preset area, processing the monitoring data by adopting a concave-convex lens image increasing algorithm, acquiring processed monitoring data, and extracting face information characteristics of the processed monitoring data to acquire face information data;
the calculation module is used for extracting the characteristic data in the processed face information data, comparing the characteristic data with the characteristic data in the pre-stored object data, judging and distinguishing the class object data matched with the pre-stored object data, and calculating the data proportion of each class object data in the pre-stored object data;
the early warning module is used for judging that the proportion of the data exceeds a preset data proportion value and generating early warning information;
the processing the face information data according to the ridgelet transform and the wavelet threshold function algorithm to obtain the processed face information data comprises the following steps:
(1) carrying out wavelet decomposition on the noisy face information and denoising by using an improved wavelet threshold number;
(2) reconstructing an original image by using the denoised wavelet coefficient;
(3) carrying out Radon transformation on the noisy face information and carrying out wavelet transformation on a Radon matrix obtained by transformation;
(4) denoising a matrix obtained by wavelet transformation by using an improved wavelet threshold function;
(5) performing wavelet inverse transformation on the matrix subjected to denoising;
(6) performing Radon inverse transformation on the matrix obtained by the wavelet inverse transformation;
(7) and fusing the image obtained by denoising the wavelet threshold and the image obtained by denoising the ridge wave.
8. The campus monitoring equipment based on the intelligent camera is characterized by comprising a processor and a memory for storing executable instructions of the processor; wherein the processor is configured to:
acquiring monitoring data in a predetermined area;
processing the monitoring data by adopting a concave-convex lens image increasing algorithm, acquiring the processed monitoring data, and extracting the face information characteristics of the processed monitoring data to acquire the face information data;
processing the face information data according to a ridgelet transform and wavelet threshold function algorithm to obtain the processed face information data, wherein the processing comprises the following steps:
(1) carrying out wavelet decomposition on the noisy face information and denoising by using an improved wavelet threshold number;
(2) reconstructing an original image by using the denoised wavelet coefficient;
(3) carrying out Radon transformation on the noisy face information and carrying out wavelet transformation on a Radon matrix obtained by transformation;
(4) denoising a matrix obtained by wavelet transformation by using an improved wavelet threshold function;
(5) performing wavelet inverse transformation on the matrix after denoising;
(6) performing Radon inverse transformation on the matrix obtained by wavelet inverse transformation;
(7) fusing an image obtained by denoising the wavelet threshold and an image obtained by denoising the ridge wave;
extracting characteristic data in the processed face information data, comparing the characteristic data with characteristic data in prestored object data, and judging and distinguishing class object data matched with the prestored object data;
calculating the data proportion of each category of object data in the pre-stored object data;
and judging that the data proportion exceeds a preset data proportion value, and generating early warning information.
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| CN106529570A (en) * | 2016-10-14 | 2017-03-22 | 西安电子科技大学 | Image classification method based on deep ridgelet neural network |
| CN106713857A (en) * | 2016-12-15 | 2017-05-24 | 重庆凯泽科技股份有限公司 | Campus security system and method based on intelligent videos |
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| CN106529570A (en) * | 2016-10-14 | 2017-03-22 | 西安电子科技大学 | Image classification method based on deep ridgelet neural network |
| CN106713857A (en) * | 2016-12-15 | 2017-05-24 | 重庆凯泽科技股份有限公司 | Campus security system and method based on intelligent videos |
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