CN110853272A - Bus safety monitoring method, device, equipment and storage medium - Google Patents
Bus safety monitoring method, device, equipment and storage medium Download PDFInfo
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- CN110853272A CN110853272A CN201910930443.3A CN201910930443A CN110853272A CN 110853272 A CN110853272 A CN 110853272A CN 201910930443 A CN201910930443 A CN 201910930443A CN 110853272 A CN110853272 A CN 110853272A
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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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- 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
- G08B13/19613—Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
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- Engineering & Computer Science (AREA)
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Abstract
The invention discloses a bus safety monitoring method, a device, equipment and a storage medium, comprising the following steps: capturing a real-time picture in the bus through a camera; coding the video data in the real-time picture, and transmitting the video data to a safety monitoring system at a server end; the safety monitoring system extracts the characteristics of the video data through a convolutional neural network; and the safety monitoring system matches the extracted features with the abnormal behavior features learned by the trained model, and obtains a feature matching result. According to the invention, the camera can capture abnormal conditions in the vehicle in real time, the video data is processed and then transmitted to the safety monitoring system for feature extraction and feature matching, and corresponding operation is executed according to the obtained feature matching result. The invention can detect and process abnormal behaviors, thereby ensuring the safety of passengers in the vehicle in the driving process. The warning function is played for some illegal criminal behaviors, and the peace and the development of the society are facilitated.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to a bus safety monitoring method, device, equipment and storage medium.
Background
The green trip is a trip mode called for in the current time and is also a main choice for people to trip in the future, and the bus is one of representatives of the green trip. At present, the safety problem of traveling becomes one of the most concerned problems of traveling of people, and although the safety of taking a bus is higher, some abnormal conditions, such as theft, civilization of passengers, sudden situations of passengers in the bus in the traveling process, and the like, inevitably occur in the traveling process of the bus. In the case of manual ticketing, some abnormal situations can be perceived by a ticket seller, but the ticket seller cannot perceive all the abnormal situations. Under the condition of unmanned ticketing, the abnormal condition is more difficult to be perceived. The existence of these abnormal conditions can bring certain safety risk to passengers and drivers in the bus in the process of driving the bus to a certain extent.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a bus safety monitoring method, device, equipment and storage medium, which can find abnormal behavior conditions in a bus in time and take corresponding measures, thereby reducing the safety risk of personnel in the bus.
According to the first aspect of the invention, the bus safety monitoring method comprises the following steps:
capturing a real-time picture in the bus through a camera;
coding the video data in the real-time picture, and transmitting the video data to a safety monitoring system at a server end;
the safety monitoring system extracts the characteristics of the video data through a convolutional neural network;
and the safety monitoring system matches the extracted features with the abnormal behavior features learned by the trained model and obtains a feature matching result.
The bus safety monitoring method provided by the embodiment of the invention at least has the following beneficial effects: according to the invention, the camera can capture abnormal conditions in the vehicle in real time, the video data is processed and then transmitted to the safety monitoring system for feature extraction and feature matching, and corresponding operation is executed according to the obtained feature matching result. The invention can monitor the condition in the vehicle in real time, detect abnormal behavior in time and process the abnormal behavior, thereby ensuring the safety of passengers in the vehicle in the driving process. The warning function is played for some illegal criminal behaviors, and the peace and the development of the society are facilitated.
According to some embodiments of the invention, further comprising the steps of:
judging whether the feature matching result is successful or not;
and when the action is successful, judging the action to be abnormal and giving an alarm.
According to some embodiments of the invention, further comprising the steps of:
and when the operation is unsuccessful, judging the operation to be normal.
According to the second aspect of the invention, the bus safety monitoring device comprises:
the capturing unit is used for capturing real-time pictures in the bus through the camera;
the processing and transmitting unit is used for encoding the video data in the real-time picture and transmitting the encoded video data to a safety monitoring system at a server end;
the characteristic extraction unit is used for the safety monitoring system to extract the characteristics of the video data through a convolutional neural network;
and the matching unit is used for matching the extracted features with the abnormal behavior features learned by the trained model by the safety monitoring system and obtaining a feature matching result.
According to some embodiments of the present invention, the matching unit is further configured to determine whether the feature matching result is successful; secondly, bus safety monitoring device still includes:
the abnormal behavior confirmation unit is used for judging the abnormal behavior and giving an alarm when the abnormal behavior is successful;
and the normal behavior confirmation unit is used for judging the normal behavior when the operation is unsuccessful.
The bus safety monitoring device according to the third aspect of the embodiment of the invention comprises at least one control processor and a memory which is used for being connected with the at least one control processor in a communication way; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the bus safety monitoring method of the first aspect as described above.
According to a fourth aspect of the present invention, there is provided a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method for bus safety monitoring as described in the first aspect above.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a bus safety monitoring method according to an embodiment of the invention;
fig. 2 is a structural diagram of a convolutional neural network used in the bus safety monitoring method according to the embodiment of the present invention;
FIG. 3 is a block diagram of a bus safety monitoring device according to an embodiment of the present invention;
FIG. 4 is a block diagram of a bus safety monitoring device according to an embodiment of the present invention;
reference numerals:
the bus safety monitoring device comprises a bus safety monitoring device 100, a capturing unit 110, a processing and transmitting unit 120, a feature extracting unit 130, a matching unit 140, an abnormal behavior confirming unit 150 and a normal behavior confirming unit 160;
bus safety monitoring device 200, control processor 210, memory 220.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
Referring to fig. 1, a bus safety monitoring method according to an embodiment of a first aspect of the present invention includes the following steps:
s1: capturing a real-time picture in the bus through a camera; the camera can be high definition digtal camera or super clear digtal camera, and secondly the quantity of camera can be one or more.
S2: coding the video data in the real-time picture, and transmitting the video data to a safety monitoring system at a server end;
s3: the safety monitoring system extracts the characteristics of the video data through a convolutional neural network;
s4: and the safety monitoring system matches the extracted features with the abnormal behavior features learned by the trained model and obtains a feature matching result.
The bus safety monitoring method provided by the embodiment of the invention at least has the following beneficial effects: according to the invention, the camera can capture abnormal conditions in the vehicle in real time, the video data is processed and then transmitted to the safety monitoring system for feature extraction and feature matching, and corresponding operation is executed according to the obtained feature matching result. The invention can monitor the condition in the vehicle in real time, detect abnormal behavior in time and process the abnormal behavior, thereby ensuring the safety of passengers in the vehicle in the driving process. The warning function is played for some illegal criminal behaviors, and the peace and the development of the society are facilitated.
According to some embodiments of the invention, further comprising the steps of:
s5: judging whether the feature matching result is successful or not;
s6 a: and when the action is successful, judging the action to be abnormal and giving an alarm.
The above abnormal behavior and corresponding warning operation are shown in table 1 below:
TABLE 1
According to some embodiments of the invention, further comprising the steps of:
s6 b: and when the operation is unsuccessful, judging the operation to be normal.
Referring to fig. 2, the CNN network herein adopts a packet convolution technique, and extracts motion features of a human body by applying a variable convolution kernel to the convolution layers C1 and C2 to obtain data of motion features of the human body. Meanwhile, in order to reduce the calculation amount of excessive parameter increase, the parameter amount is reduced by applying 1x1 convolution kernels in the convolution layer, so that the calculation amount is reduced, and the result is more accurate and reliable. And after packet convolution, pooling is carried out, redundant data are processed, and action characteristic data are further extracted from a sampling layer. And finally, converting information obtained by processing all convolution kernels into one-dimensional vectors through a full-connection layer, and entering an output layer through a plurality of fully-connected feedforward neural network layers (FNN) so as to predict a final output result.
The bus safety monitoring system lists some abnormal behaviors encountered during the operation of taking a bus at ordinary times, analyzes and extracts the characteristics of the abnormal behaviors, and stores the extracted characteristics in an abnormal database (database). In the operation process of the bus, data transmitted by the high-definition camera is processed by CNN to obtain a characteristic value, the obtained characteristic value is matched with the abnormal behavior characteristic learned by the trained model, and whether the behavior analyzed and processed by the CNN is the abnormal behavior is judged according to the similarity of the characteristic value and the abnormal behavior characteristic. And finally, executing corresponding operation according to the matching result, and warning the behavior if the behavior is abnormal.
A structural similarity algorithm (SSIM) is used for feature matching. The method comprises the following steps:
apply the following formula:
wherein u isX、uYRepresenting the mean, σ, of images X and Y, respectivelyX、σYRespectively representing the standard deviation, σ, of the images X and YX 2、σY 2Representing the variance of images X and Y, respectively. SigmaXYRepresenting the covariance of images X and Y. C1、C2、C3Is a constant. Adding C into the denominator1、C2、C3This is to avoid the case where the denominator occurrence value is 0. Usually take C1＝(K1L)2，C2＝(K2L)2，
Wherein K1＝0.01，K2When L is 0.03, L is 255(L is a dynamic range of a pixel value, and is generally 255).
The SSIM index obtained by calculation is as follows: SSIM (X, Y) ═ L (X, Y) × C (X, Y) × S (X, Y)
When setting up
When, the formula can be rewritten as follows:
the structural similarity index defines information from an image composition perspective as attributes reflecting the structure of objects in a scene independent of brightness, contrast, and models distortion as a combination of three different factors of brightness, contrast, and structure. The mean is used as an estimate of the luminance, the standard deviation as an estimate of the contrast, and the covariance as a measure of the structural similarity.
The formula for solving the mean, the variance and the standard deviation is as follows:
in practical application, a gaussian function is generally used to calculate the mean, variance and covariance of an image, rather than a mode of traversing pixel points, so as to achieve higher efficiency.
Referring to fig. 3, a bus safety monitoring device 100 according to a second aspect of the embodiment of the present invention includes:
the capturing unit 110 is used for capturing real-time pictures in the bus through a camera;
the processing and transmitting unit 120 is configured to encode the video data in the real-time picture and transmit the encoded video data to a security monitoring system at a server;
a feature extraction unit 130, configured to perform feature extraction on the video data through a convolutional neural network by the security monitoring system;
and the matching unit 140 is used for the safety monitoring system to match the extracted features with the abnormal behavior features learned by the trained model, and obtain a feature matching result.
It should be noted that, since the bus safety monitoring device 100 in the embodiment is based on the same inventive concept as the bus safety monitoring method, the corresponding content in the method embodiment is also applicable to the embodiment of the device, and is not described in detail herein.
According to some embodiments of the present invention, the matching unit is further configured to determine whether the feature matching result is successful; secondly, the bus safety monitoring device 100 further includes:
an abnormal behavior confirmation unit 150, configured to determine an abnormal behavior and warn when the abnormal behavior is successful;
and a normal behavior confirmation unit 160 for determining a normal behavior when the determination is unsuccessful.
Referring to fig. 4, according to the bus safety monitoring device 200 of the third aspect of the present invention, the bus safety monitoring device 200 may be any type of intelligent terminal, such as a mobile phone, a tablet computer, a personal computer, and the like.
Specifically, the bus safety monitoring device 200 includes: one or more control processors 210 and memory 220, one control processor 210 being illustrated in fig. 4.
The control processor 210 and the memory 220 may be connected by a bus or other means, such as the bus connection in fig. 4.
The memory 220, which is a non-transitory computer readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the bus safety monitoring method in the embodiment of the present invention, for example, the unit 110 and 160 shown in fig. 3. The control processor 210 executes various functional applications and data processing of the bus safety monitoring device 100 by running the non-transitory software programs, instructions and modules stored in the memory 220, that is, implements the bus safety monitoring method of the above-described method embodiment.
The memory 220 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the bus safety monitoring device 100, and the like. Further, the memory 220 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 220 may optionally include memory 220 remotely located from the control processor 210, and the remote memory 220 may be connected to the bus safety monitoring device 200 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 220 and, when executed by the one or more control processors 210, perform the bus safety monitoring method in the above-described method embodiments, for example, perform the above-described method steps S1-S6 b in fig. 1, and implement the functions of the unit 110 and 160 in fig. 3.
According to the computer-readable storage medium of the fourth embodiment of the present invention, the computer-readable storage medium stores computer-executable instructions, which are executed by one or more control processors 210, for example, by one control processor 210 in fig. 4, and can make the one or more control processors 210 execute the bus safety monitoring method in the above-mentioned method embodiment, for example, execute the above-mentioned method steps S1 to S6b in fig. 1, and implement the functions of unit 110 and unit 160 in fig. 3.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by software plus a general hardware platform. Those skilled in the art will appreciate that all or part of the processes of the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (7)
1. A bus safety monitoring method is characterized by comprising the following steps:
capturing a real-time picture in the bus through a camera;
coding the video data in the real-time picture, and transmitting the video data to a safety monitoring system at a server end;
the safety monitoring system extracts the characteristics of the video data through a convolutional neural network;
and the safety monitoring system matches the extracted features with the abnormal behavior features learned by the trained model and obtains a feature matching result.
2. The bus safety monitoring method according to claim 1, further comprising the steps of:
judging whether the feature matching result is successful or not;
and when the action is successful, judging the action to be abnormal and giving an alarm.
3. The bus safety monitoring method according to claim 2, further comprising the steps of:
and when the operation is unsuccessful, judging the operation to be normal.
4. A bus safety monitoring device, comprising:
the capturing unit is used for capturing real-time pictures in the bus through the camera;
the processing and transmitting unit is used for encoding the video data in the real-time picture and transmitting the encoded video data to a safety monitoring system at a server end;
the characteristic extraction unit is used for the safety monitoring system to extract the characteristics of the video data through a convolutional neural network;
and the matching unit is used for matching the extracted features with the abnormal behavior features learned by the trained model by the safety monitoring system and obtaining a feature matching result.
5. The bus safety monitoring device according to claim 4, wherein the matching unit is further configured to determine whether the feature matching result is successful; secondly, bus safety monitoring device still includes:
the abnormal behavior confirmation unit is used for judging the abnormal behavior and giving an alarm when the abnormal behavior is successful;
and the normal behavior confirmation unit is used for judging the normal behavior when the operation is unsuccessful.
6. The utility model provides a bus safety monitoring equipment which characterized in that: comprises at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the bus safety monitoring method of any one of claims 1 to 3.
7. A computer-readable storage medium characterized by: the computer-readable storage medium stores computer-executable instructions for causing a computer to perform the bus safety monitoring method according to any one of claims 1 to 3.
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| CN201910930443.3A CN110853272A (en) | 2019-09-29 | 2019-09-29 | Bus safety monitoring method, device, equipment and storage medium |
| PCT/CN2020/112527 WO2021057393A1 (en) | 2019-09-29 | 2020-08-31 | Bus safety monitoring method, apparatus, and device, and storage medium |
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| CN201910930443.3A CN110853272A (en) | 2019-09-29 | 2019-09-29 | Bus safety monitoring method, device, equipment and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112434564A (en) * | 2020-11-04 | 2021-03-02 | 北方工业大学 | Detection system for abnormal aggregation behaviors in bus |
| WO2021057393A1 (en) * | 2019-09-29 | 2021-04-01 | 五邑大学 | Bus safety monitoring method, apparatus, and device, and storage medium |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114575629B (en) * | 2022-01-22 | 2023-06-30 | 昆山市交通场站管理有限公司 | Bus shelter monitored control system based on cloud |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106878670A (en) * | 2016-12-24 | 2017-06-20 | 深圳云天励飞技术有限公司 | A kind of method for processing video frequency and device |
| CN206421210U (en) * | 2016-12-30 | 2017-08-18 | 孙国强 | A kind of pair of spectrum intelligent bus monitoring system |
| CN108846365A (en) * | 2018-06-24 | 2018-11-20 | 深圳市中悦科技有限公司 | It fights in video detection method, device, storage medium and the processor of behavior |
| CN109241946A (en) * | 2018-10-11 | 2019-01-18 | 平安科技（深圳）有限公司 | Abnormal behaviour monitoring method, device, computer equipment and storage medium |
| CN109376634A (en) * | 2018-10-15 | 2019-02-22 | 北京航天控制仪器研究所 | A kind of Bus driver unlawful practice detection system neural network based |
| CN109389107A (en) * | 2019-01-15 | 2019-02-26 | 深兰人工智能芯片研究院（江苏）有限公司 | Vehicle safety processing method, device and computer readable storage medium |
| CN109685516A (en) * | 2019-01-17 | 2019-04-26 | 深兰科技（上海）有限公司 | A kind of bus |
| CN110008867A (en) * | 2019-03-25 | 2019-07-12 | 五邑大学 | A kind of method for early warning based on personage's abnormal behaviour, device and storage medium |
| CN110103820A (en) * | 2019-04-24 | 2019-08-09 | 深圳市轱辘汽车维修技术有限公司 | The method, apparatus and terminal device of the abnormal behaviour of personnel in a kind of detection vehicle |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9665802B2 (en) * | 2014-11-13 | 2017-05-30 | Nec Corporation | Object-centric fine-grained image classification |
| CN108073886B (en) * | 2016-11-18 | 2019-08-09 | 南京行者易智能交通科技有限公司 | A kind of passenger on public transport amount evaluation method based on machine learning |
| CN107145819A (en) * | 2017-03-14 | 2017-09-08 | 浙江宇视科技有限公司 | A kind of bus crowding determines method and apparatus |
| CN110853272A (en) * | 2019-09-29 | 2020-02-28 | 五邑大学 | Bus safety monitoring method, device, equipment and storage medium |
-
2019
- 2019-09-29 CN CN201910930443.3A patent/CN110853272A/en active Pending
-
2020
- 2020-08-31 WO PCT/CN2020/112527 patent/WO2021057393A1/en active Application Filing
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106878670A (en) * | 2016-12-24 | 2017-06-20 | 深圳云天励飞技术有限公司 | A kind of method for processing video frequency and device |
| CN206421210U (en) * | 2016-12-30 | 2017-08-18 | 孙国强 | A kind of pair of spectrum intelligent bus monitoring system |
| CN108846365A (en) * | 2018-06-24 | 2018-11-20 | 深圳市中悦科技有限公司 | It fights in video detection method, device, storage medium and the processor of behavior |
| CN109241946A (en) * | 2018-10-11 | 2019-01-18 | 平安科技（深圳）有限公司 | Abnormal behaviour monitoring method, device, computer equipment and storage medium |
| CN109376634A (en) * | 2018-10-15 | 2019-02-22 | 北京航天控制仪器研究所 | A kind of Bus driver unlawful practice detection system neural network based |
| CN109389107A (en) * | 2019-01-15 | 2019-02-26 | 深兰人工智能芯片研究院（江苏）有限公司 | Vehicle safety processing method, device and computer readable storage medium |
| CN109685516A (en) * | 2019-01-17 | 2019-04-26 | 深兰科技（上海）有限公司 | A kind of bus |
| CN110008867A (en) * | 2019-03-25 | 2019-07-12 | 五邑大学 | A kind of method for early warning based on personage's abnormal behaviour, device and storage medium |
| CN110103820A (en) * | 2019-04-24 | 2019-08-09 | 深圳市轱辘汽车维修技术有限公司 | The method, apparatus and terminal device of the abnormal behaviour of personnel in a kind of detection vehicle |
Non-Patent Citations (4)
| Title |
|---|
| ZHOU WANG.ETC: ""Image Quality Assessment: From Error Visibility to Structural Similarity"", 《IEEE TRANSACTIONS ON IMAGE PROCESSING》 * |
| 吴参毅: ""安防领域人工智能深度神经网络算法的创新突破"", 《中国安防》 * |
| 沈铮: ""基于图像处理的公交车内人群异常情况监测"", 《计算机工程与设计》 * |
| 沈铮: "《中国优秀硕士学位论文全文数据库信息科技辑》", 15 February 2018 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021057393A1 (en) * | 2019-09-29 | 2021-04-01 | 五邑大学 | Bus safety monitoring method, apparatus, and device, and storage medium |
| CN112434564A (en) * | 2020-11-04 | 2021-03-02 | 北方工业大学 | Detection system for abnormal aggregation behaviors in bus |
| CN112434564B (en) * | 2020-11-04 | 2023-06-27 | 北方工业大学 | Detection system for abnormal aggregation behavior in bus |
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