CN110956143A - Abnormal behavior detection method and device, electronic equipment and storage medium - Google Patents
Abnormal behavior detection method and device, electronic equipment and storage medium Download PDFInfo
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- CN110956143A CN110956143A CN201911222657.1A CN201911222657A CN110956143A CN 110956143 A CN110956143 A CN 110956143A CN 201911222657 A CN201911222657 A CN 201911222657A CN 110956143 A CN110956143 A CN 110956143A
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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
- G06V40/20—Movements or behaviour, e.g. gesture recognition
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
Abstract
The embodiment of the invention discloses an abnormal behavior detection method, an abnormal behavior detection device, electronic equipment and a storage medium, wherein the abnormal behavior detection method comprises the following steps: acquiring real-time passenger data of a current carriage and preset reference passenger data, wherein the preset reference passenger data comprises passenger data when abnormal behaviors and abnormal behaviors do not exist in the current carriage; and determining whether the current carriage has abnormal behaviors or not based on the real-time passenger data and the preset reference passenger data. By adopting the invention, the consumption of human resources can be effectively saved, the operation cost is reduced, the safety of passengers is effectively improved, and the train operation efficiency is improved.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to an abnormal behavior detection method and device, electronic equipment and a storage medium.
Background
With the continuous development of urban rail transit construction, more and more passengers select rail transit means such as subways and high-speed rails to go out. In the train operation process, abnormal behaviors such as fighting and faint often occur, and the personal safety of passengers is seriously influenced.
At this stage, in order to prevent abnormal behavior, operators usually arrange multiple stations to patrol each car of each train in real time. When the station service patrols that the carriage has abnormal behavior, the station service can inform the operator, and the operator performs coordination processing to ensure the personal safety of passengers. Thus, since the train is usually formed by connecting a plurality of cars, the stations are used to patrol each car, which consumes a lot of human resources, and results in higher operation cost for operators.
Disclosure of Invention
Because the existing methods have the above problems, embodiments of the present invention provide a method and an apparatus for detecting abnormal behavior, an electronic device, and a storage medium.
In a first aspect, an embodiment of the present invention provides an abnormal behavior detection method, including:
acquiring real-time passenger data of a current carriage and preset reference passenger data, wherein the preset reference passenger data comprises passenger data when abnormal behaviors and abnormal behaviors do not exist in the current carriage;
and determining whether the current carriage has abnormal behaviors or not based on the real-time passenger data and the preset reference passenger data.
Optionally, the passenger data includes one or both of passenger image data and passenger sound data.
Optionally, when the passenger data is passenger image data, the determining whether the current car has an abnormal behavior based on the real-time passenger data and the preset reference passenger data includes:
constructing a multilayer neural network, and generating an image detection model based on the multilayer neural network and the preset reference passenger image data;
and determining whether the current compartment has abnormal behaviors or not based on the image detection model and the real-time passenger image data.
Optionally, when the passenger data is passenger sound data, the determining whether the current car has an abnormal behavior based on the real-time passenger data and the preset reference passenger data includes:
determining sensitive words appearing when abnormal behaviors occur based on the preset reference passenger sound data, and constructing a sensitive word bank based on the sensitive words appearing when the abnormal behaviors occur;
judging whether real-time words appearing in the real-time passenger sound data exist in the sensitive word bank or not;
and if the current compartment exists in the sensitive word stock, determining that the current compartment has abnormal behaviors.
Optionally, when the passenger data includes passenger image data and passenger sound data, determining whether there is an abnormal behavior in the current car based on the real-time passenger data and the preset reference passenger data includes:
determining whether the current compartment has abnormal behaviors or not based on the real-time passenger image data and the preset reference passenger image data to obtain a first determination result;
determining whether the current carriage has abnormal behaviors or not based on the real-time passenger sound data and the preset reference passenger sound data to obtain a second determination result;
determining whether there is an abnormal behavior in the current car by a joint probability density algorithm based on the first determination result and the second determination result.
Optionally, when there is an abnormal behavior in the current car, the determination result at least includes information of a position where the abnormal behavior occurs.
Optionally, the determining whether there is an abnormal behavior in the current car further includes:
and if the current carriage has abnormal behaviors, determining the identification number of the current carriage, generating an alarm message based on the identification number, and sending the alarm message.
In a second aspect, an embodiment of the present invention further provides an abnormal behavior detection apparatus, including an obtaining module and a processing module, where:
the acquisition module is used for acquiring real-time passenger data of a current carriage and preset reference passenger data, wherein the preset reference passenger data comprises passenger data when abnormal behaviors and abnormal behaviors do not exist in the current carriage;
and the processing module is used for determining whether the current carriage has abnormal behaviors or not based on the real-time passenger data and the preset reference passenger data.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, which when called by the processor are capable of performing the above-described methods.
In a fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium storing a computer program, which causes the computer to execute the above method.
According to the technical scheme, whether abnormal behaviors exist in the carriage is determined by acquiring the real-time passenger data and the preset reference passenger data of the carriage and based on the real-time passenger data and the preset reference passenger data. Therefore, on one hand, whether the carriage has abnormal behaviors or not is automatically determined, so that the consumption of human resources can be effectively saved, and the operation cost is reduced. On the other hand, the misjudgment occurring in the manual confirmation can be avoided, so that the safety of passengers can be effectively improved, and the train operation efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a detection system according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of an abnormal behavior detection method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an abnormal behavior detection apparatus according to an embodiment of the present invention;
fig. 4 is a logic block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
An execution main body of the method can be a detection system, referring to fig. 1, the detection system can include a camera, a sound collection sensor and a control host computer, the camera, the sound collection sensor and the control host computer are arranged in each carriage, the control host computer can acquire passenger data collected by the camera and the sound collection sensor, and data processing is carried out on the passenger data to determine whether abnormal behaviors exist in the carriage. Wherein, camera and sound acquisition sensor can install in every section carriage, and every section carriage can select to install one or more camera and sound acquisition sensor according to actual conditions. Each train can be provided with a control host, such as a cab of the train.
Fig. 2 shows a schematic flow chart of an abnormal behavior detection method provided in this embodiment, including:
s201, acquiring real-time passenger data of the current compartment and presetting reference passenger data.
Wherein, the current compartment refers to any compartment needing to detect whether abnormal behaviors exist.
The passenger data includes one or both of passenger image data and passenger sound data.
The preset reference passenger data comprises passenger data when abnormal behaviors and abnormal behaviors are absent in the current carriage, wherein the abnormal behaviors can be behaviors such as fighting and faint.
In implementation, whether abnormal behaviors exist in the current compartment or not can be determined based on the real-time passenger data of the current compartment and the preset reference passenger data, so that the abnormal behaviors of part of passengers are prevented from influencing the personal safety of other passengers. Specifically, first, real-time passenger data of the current car may be acquired, such as one or both of passenger image data and passenger sound data. Then, the preset reference passenger data corresponding to the current compartment can be acquired.
S202, determining whether the current compartment has abnormal behaviors or not based on the real-time passenger data and the preset reference passenger data.
In implementation, after the real-time passenger data and the preset reference passenger data of the current car are acquired, data processing may be performed on the acquired real-time passenger data and the preset reference passenger data to determine whether an abnormal behavior exists in the current car. Then, different messages can be sent according to the determination result, namely whether the abnormal behavior exists in the current compartment, so that the operator can take different measures according to the different messages to prevent the abnormal behavior from influencing the personal safety of passengers.
According to the technical scheme, whether abnormal behaviors exist in the carriage is determined by acquiring the real-time passenger data and the preset reference passenger data of the carriage and based on the real-time passenger data and the preset reference passenger data. Therefore, on one hand, whether the carriage has abnormal behaviors or not is automatically determined, so that the consumption of human resources can be effectively saved, and the operation cost is reduced. On the other hand, the misjudgment of the manual patrol can be avoided, so that the safety of passengers can be effectively improved, and the train operation efficiency is improved.
Further, on the basis of the above method embodiment, it may be determined whether there is an abnormal behavior in the current car based on the real-time passenger image data and the preset reference passenger image data, and the corresponding processing of step S202 may be as follows: constructing a multilayer neural network, and generating an image detection model based on the multilayer neural network and preset reference passenger image data; and determining whether the current compartment has abnormal behaviors or not based on the image detection model and the real-time passenger image data.
In implementation, when the passenger data is the passenger image data, that is, the real-time passenger data is the real-time passenger image data, and the preset reference data is the preset reference passenger image data, first, the preset reference passenger image data when the current car has abnormal behavior and no abnormal behavior within the preset duration before the current time may be obtained, and sample data is formed based on the preset reference passenger image data. Then, a multilayer neural network can be constructed, and the multilayer neural network is used for training the sample data to generate an image detection model. If the abnormal behavior and abnormal behavior are existed in the sample data, the image characteristics can be extracted based on the characteristic extraction algorithm, and the image characteristics can be trained based on deep learning to identify the abnormal behavior of the passenger in the current carriage; then, the image characteristics of the abnormal behavior and the abnormal behavior can be compared, so that an image detection model can be constructed according to different influences of the different characteristics on a determination result (namely, the determination result of whether the abnormal behavior exists in the current compartment), and the model can include corresponding motion modes when the abnormal behavior occurs, such as a four-way gathering mode, a four-way scattering mode, a rapid and continuous action difference mode and an attitude abnormality mode. After the image detection model is constructed, whether abnormal behaviors exist in the current carriage or not can be determined based on the real-time passenger image data and the image detection model, so that coordination processing can be timely performed when the abnormal behaviors exist, and the influence of the abnormal behaviors on the personal safety of passengers is reduced. If the motion trajectory line of the real-time passenger image data is matched with any one of the four motion modes, a minimum matching value can be set, and when the actual matching value of the motion trajectory line of the real-time passenger image data and any one motion mode is greater than or equal to the minimum matching value, the current compartment is considered to have abnormal behavior. Therefore, based on the real-time passenger image data and the image detection model, whether the current carriage has abnormal behaviors or not can be determined quickly and intuitively in advance, and the influence of the abnormal behaviors on the personal safety of passengers can be further reduced, so that the safety of the passengers and the train operation efficiency are further improved.
Further, on the basis of the above method embodiment, it may be determined whether there is abnormal behavior in the current car based on the real-time passenger sound data and the preset reference passenger sound data, and the corresponding processing of step S202 may be as follows: determining sensitive words appearing when abnormal behaviors occur based on preset reference passenger sound data, and constructing a sensitive word bank based on the sensitive words appearing when the abnormal behaviors occur; judging whether real-time words appearing in the real-time passenger sound data exist in a sensitive word bank or not; and if the abnormal behavior exists in the sensitive word stock, determining that the abnormal behavior exists in the current compartment.
In implementation, a sensitive word bank may be constructed based on preset reference passenger sound data, for example, all sensitive words occurring when an abnormal behavior occurs may be extracted, and the sensitive word bank may be constructed based on all the sensitive words. Then, the real-time words appearing in the real-time passenger voice data can be extracted, and whether the real-time words have words existing in the sensitive word stock can be judged. For example, after the real-time passenger sound data of the current compartment is collected by the array type sound collection sensor, signal preprocessing (such as reverberation suppression, array enhancement and the like) can be performed on the real-time passenger sound data to filter out irrelevant data (such as compartment noise and the like), and clear and distinguishable passenger sound data of the detection system can be extracted. Then, real-time vocabularies in the passenger voice data after the signal preprocessing can be collected through a voice recognition algorithm to determine whether the real-time vocabularies exist in a sensitive word bank, namely whether the real-time vocabularies exist in the sensitive word bank is determined through keyword recognition. If the vocabulary exists in the sensitive word stock, the abnormal behavior of the current compartment can be determined. Otherwise, it may be determined that there is no abnormal behavior in the current car. It can be understood that when abnormal behaviors occur, the decibel value of sound is generally higher, so that the decibel value corresponding to each sensitive word can be extracted when the sensitive word library is constructed, and the sensitive word library is constructed based on all the sensitive words and the corresponding decibel values. Therefore, whether abnormal behaviors exist in the current carriage or not can be quickly determined based on the real-time passenger sound data and the sensitive word stock, and the influence of the abnormal behaviors on the personal safety of the passengers can be further reduced, so that the safety of the passengers and the train operation efficiency are further improved.
Further, on the basis of the above method embodiment, it may be determined whether there is an abnormal behavior in the current car based on the passenger image data and the passenger sound data, and the corresponding processing of step S202 may be as follows: determining whether the current compartment has abnormal behaviors or not based on the real-time passenger image data and the preset reference passenger image data to obtain a first determination result; determining whether the current carriage has abnormal behaviors or not based on the real-time passenger sound data and the preset reference passenger sound data to obtain a second determination result; whether abnormal behavior exists in the current car is determined by a joint probability density algorithm based on the first determination result and the second determination result.
When the current carriage has abnormal behaviors, the determination result at least can include the position information of the abnormal behaviors, or can also include specific types of the abnormal behaviors, such as fighting and the like.
In practice, the result of determining whether there is an abnormal behavior in the vehicle cabin may not be accurate based on the passenger sound data or the passenger image data alone, considering that the vehicle cabin may have dim lights. Therefore, whether the current compartment has abnormal behavior can be determined based on the passenger image data and the passenger sound data. Specifically, when the passenger data includes passenger image data and passenger sound data, that is, the real-time passenger data includes real-time passenger image data and real-time passenger sound data, and the preset reference data includes preset reference passenger image data and preset reference passenger sound data, first, it may be determined whether there is an abnormal behavior in the current car, that is, a first determination result, based on the real-time passenger image data and the preset reference passenger image data. Then, it may be determined whether there is an abnormal behavior in the current car, i.e., a second determination result, based on the aforementioned real-time passenger sound data and preset reference passenger sound data. Then, based on the first determination result and the second determination result, further analysis can be performed through a joint probability density algorithm, and whether abnormal behaviors exist in the current compartment or not can be determined. Therefore, the first determination result and the second determination result are combined, information fusion is carried out through a joint probability density algorithm, whether abnormal behaviors exist in the current compartment or not is determined, the accuracy of the determination result can be further improved, the influence of the abnormal behaviors on the personal safety of passengers can be further reduced, the safety of the passengers can be further improved, and the operation efficiency is improved.
Further, on the basis of the above method embodiment, when it is determined that there is an abnormal behavior in the current car, an alarm message may be sent, and the corresponding processing may be as follows: and if the current carriage has abnormal behaviors, determining the identification number of the current carriage, generating an alarm message based on the identification number, and sending the alarm message.
In an implementation, when it is determined that there is abnormal behavior in the current car, the identification number of the current car may be acquired, which may be the number of the current car. Then, an alarm message may be generated based on the number of the current car, and the alarm message may carry at least the identification number of the current car and information indicating that there is an abnormal behavior in the current car. And then, the alarm message can be sent, for example, the alarm message can be sent to a train control system, so that a worker can remind an adjacent operator and law enforcement personnel to get on the train nearby to stop abnormal behaviors. Meanwhile, passenger sound data and passenger image data can be continuously collected and stored, and evidences are kept. In this way, passenger safety and operational efficiency can be further improved.
Fig. 3 shows an abnormal behavior detection apparatus provided in this embodiment, which includes an obtaining module 301 and a processing module 302, where:
the obtaining module 301 is configured to obtain real-time passenger data of a current car and preset reference passenger data, where the preset reference passenger data includes passenger data when there is an abnormal behavior and no abnormal behavior in the current car;
the processing module 302 is configured to determine whether an abnormal behavior exists in the current car based on the real-time passenger data and the preset reference passenger data.
The abnormal behavior detection apparatus described in this embodiment may be used to implement the above method embodiments, and the principle and technical effect are similar, which are not described herein again.
Referring to fig. 4, the electronic device includes: a processor (processor)401, a memory (memory)402, and a bus 403;
wherein the content of the first and second substances,
the processor 401 and the memory 402 complete communication with each other through the bus 403;
the processor 401 is configured to call program instructions in the memory 402 to perform the methods provided by the above-described method embodiments.
The present embodiments disclose a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the methods provided by the above-described method embodiments.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the methods provided by the method embodiments described above.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on 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. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
It should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An abnormal behavior detection method, comprising:
acquiring real-time passenger data of a current carriage and preset reference passenger data, wherein the preset reference passenger data comprises passenger data when abnormal behaviors and abnormal behaviors do not exist in the current carriage;
and determining whether the current carriage has abnormal behaviors or not based on the real-time passenger data and the preset reference passenger data.
2. The abnormal behavior detection method according to claim 1, wherein the passenger data includes one or both of passenger image data and passenger sound data.
3. The abnormal behavior detection method according to claim 2, wherein when the passenger data is passenger image data, the determining whether the current car has the abnormal behavior based on the real-time passenger data and the preset reference passenger data includes:
constructing a multilayer neural network, and generating an image detection model based on the multilayer neural network and the preset reference passenger image data;
and determining whether the current compartment has abnormal behaviors or not based on the image detection model and the real-time passenger image data.
4. The abnormal behavior detection method according to claim 2, wherein when the passenger data is passenger sound data, the determining whether the current car has the abnormal behavior based on the real-time passenger data and the preset reference passenger data includes:
determining sensitive words appearing when abnormal behaviors occur based on the preset reference passenger sound data, and constructing a sensitive word bank based on the sensitive words appearing when the abnormal behaviors occur;
judging whether real-time words appearing in the real-time passenger sound data exist in the sensitive word bank or not;
and if the current compartment exists in the sensitive word stock, determining that the current compartment has abnormal behaviors.
5. The abnormal behavior detection method according to claim 2, wherein when the passenger data includes passenger image data and passenger sound data, the determining whether the current car has the abnormal behavior based on the real-time passenger data and the preset reference passenger data includes:
determining whether the current compartment has abnormal behaviors or not based on the real-time passenger image data and the preset reference passenger image data to obtain a first determination result;
determining whether the current carriage has abnormal behaviors or not based on the real-time passenger sound data and the preset reference passenger sound data to obtain a second determination result;
determining whether there is an abnormal behavior in the current car by a joint probability density algorithm based on the first determination result and the second determination result.
6. The abnormal behavior detection method according to claim 5, characterized in that, when there is an abnormal behavior in the current car, the determination result includes at least position information where the abnormal behavior occurs.
7. The abnormal behavior detection method according to claim 1, wherein the determining whether the abnormal behavior exists in the current car further comprises:
and if the current carriage has abnormal behaviors, determining the identification number of the current carriage, generating an alarm message based on the identification number, and sending the alarm message.
8. An abnormal behavior detection device, comprising an acquisition module and a processing module, wherein:
the acquisition module is used for acquiring real-time passenger data of a current carriage and preset reference passenger data, wherein the preset reference passenger data comprises passenger data when abnormal behaviors and abnormal behaviors do not exist in the current carriage;
and the processing module is used for determining whether the current carriage has abnormal behaviors or not based on the real-time passenger data and the preset reference passenger data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the abnormal behavior detection method according to any one of claims 1 to 7 when executing the program.
10. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the abnormal behavior detection method according to any one of claims 1 to 7.
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| CN112002102A (en) * | 2020-09-04 | 2020-11-27 | 北京伟杰东博信息科技有限公司 | Safety monitoring method and system |
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| CN110443274A (en) * | 2019-06-28 | 2019-11-12 | 平安科技（深圳）有限公司 | Method for detecting abnormality, device, computer equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112002102A (en) * | 2020-09-04 | 2020-11-27 | 北京伟杰东博信息科技有限公司 | Safety monitoring method and system |
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