CN113449693A

CN113449693A - Boarding behavior detection method and device, electronic device and storage medium

Info

Publication number: CN113449693A
Application number: CN202110838886.7A
Authority: CN
Inventors: 徐慧敏; 吴晓明; 张艳华; 吴佳飞; 张义保; 冷冰; 叶建云; 张广程; 李俊
Original assignee: Shanghai Sensetime Intelligent Technology Co Ltd
Current assignee: Shanghai Sensetime Intelligent Technology Co Ltd
Priority date: 2021-07-23
Filing date: 2021-07-23
Publication date: 2021-09-28

Abstract

The present disclosure relates to a method and apparatus for detecting an elevator riding behavior, an electronic device, and a storage medium, wherein the method includes: acquiring a video stream of a target elevator; detecting elevator riding behaviors according to the video stream, and determining a visual detection result, wherein the visual detection result is used for indicating whether abnormal elevator riding behaviors occur in the target elevator; under the condition that the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator, target equipment information of the target elevator is obtained; and verifying the at least one abnormal elevator taking behavior according to the target equipment information, and determining a target detection result, wherein the target detection result is used for indicating the target abnormal elevator taking behavior occurring in the target elevator. The embodiment of the disclosure can improve the detection accuracy of the abnormal elevator taking behavior, thereby effectively reducing the probability of elevator accidents caused by the abnormal elevator taking behavior.

Description

Boarding behavior detection method and device, electronic device and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for detecting an elevator riding behavior, an electronic device, and a storage medium.

Background

With the development of economy and the continuous improvement of infrastructure construction, the application of the elevator in the scenes such as communities, markets, office buildings, public transportation and the like is more and more extensive. Statistically, one third of elevator accidents are caused by abnormal elevator riding behaviors of passengers when riding the elevator, such as behavior of hitting the elevator door in a random manner by jumping in the elevator, bouncing in the elevator, and carrying of electric vehicles by the passengers or passengers, which causes elevator vibration, or behavior of repeatedly blocking the door of the elevator by hands or other objects. Therefore, there is a need for an elevator riding behavior detection method capable of timely and accurately detecting abnormal elevator riding behaviors in an elevator, so as to timely alarm and stop the abnormal elevator riding behaviors, thereby reducing the probability of elevator accidents.

Disclosure of Invention

The disclosure provides a method and a device for detecting elevator riding behavior, electronic equipment and a storage medium.

According to an aspect of the present disclosure, there is provided a boarding behavior detection method, including: acquiring a video stream of a target elevator; detecting elevator riding behaviors according to the video stream, and determining a visual detection result, wherein the visual detection result is used for indicating whether abnormal elevator riding behaviors occur in the target elevator; under the condition that the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator, target equipment information of the target elevator is obtained; and verifying the at least one abnormal elevator taking behavior according to the target equipment information, and determining a target detection result, wherein the target detection result is used for indicating the target abnormal elevator taking behavior occurring in the target elevator.

In a possible implementation manner, the performing elevator riding behavior detection according to the video stream and determining a visual detection result includes: carrying out target detection on the video stream, and determining a target to be tracked in the video stream; extracting the characteristics of the video stream to obtain the characteristic information to be detected corresponding to the target to be tracked; and detecting the elevator taking behavior of the characteristic information to be detected according to at least one abnormal elevator taking behavior detection network, and determining the visual detection result.

In a possible implementation manner, after performing feature extraction on the video stream to obtain to-be-detected feature information corresponding to the target to be tracked, the method further includes: determining the similarity between the characteristic information to be detected and at least one piece of prestored reference characteristic information, wherein one piece of reference characteristic information corresponds to an abnormal elevator riding behavior; and for any reference characteristic information, inputting the characteristic information to be detected into an abnormal elevator taking behavior detection network for detecting abnormal elevator taking behaviors corresponding to the reference characteristic information under the condition that the similarity between the characteristic information to be detected and the reference characteristic information is greater than a similarity threshold value.

In a possible implementation manner, the detecting the elevator-taking behavior of the feature information to be detected according to at least one abnormal elevator-taking behavior detection network, and determining the visual detection result includes: aiming at any abnormal elevator taking behavior detection network, the abnormal elevator taking behavior detection network is utilized to detect the elevator taking behavior of the characteristic information to be detected, and the prediction probability of the abnormal elevator taking behavior corresponding to the abnormal elevator taking behavior detection network in the target elevator is obtained; and determining that the abnormal elevator taking behaviors corresponding to the abnormal elevator taking behavior detection network occur in the target elevator under the condition that the prediction probability is greater than or equal to the probability threshold corresponding to the abnormal elevator taking behavior detection network.

In one possible implementation manner, the obtaining target device information of the target elevator in a case that the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator, includes: determining the behavior type of each abnormal elevator riding behavior; and acquiring the target equipment information corresponding to each abnormal elevator taking behavior according to the behavior type of each abnormal elevator taking behavior.

In a possible implementation manner, the verifying the at least one abnormal elevator riding behavior according to the target device information to determine a target detection result includes: aiming at any abnormal elevator taking behavior, determining whether the target elevator has an abnormal state corresponding to the abnormal elevator taking behavior according to the target equipment information corresponding to the abnormal elevator taking behavior; and determining the abnormal elevator taking behavior as the target abnormal elevator taking behavior when determining that the target elevator has an abnormal state corresponding to the abnormal elevator taking behavior.

In one possible implementation, the method further includes: and adjusting the running state of the target elevator according to the behavior type of the target abnormal elevator riding behavior.

In one possible implementation, the method further includes: determining preset voice prompt information corresponding to the target abnormal elevator taking behavior according to the behavior type of the target abnormal elevator taking behavior; and controlling a voice broadcasting device in the target elevator to broadcast the preset voice prompt information.

According to an aspect of the present disclosure, there is provided an elevator riding behavior detection apparatus including: the video stream acquisition module is used for acquiring the video stream of the target elevator; the elevator taking behavior detection module is used for detecting elevator taking behaviors according to the video stream and determining a visual detection result, wherein the visual detection result is used for indicating whether abnormal elevator taking behaviors occur in the target elevator; the equipment information acquisition module is used for acquiring target equipment information of the target elevator under the condition that the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator; and the checking module is used for checking the at least one abnormal elevator taking behavior according to the target equipment information and determining a target detection result, wherein the target detection result is used for indicating the target abnormal elevator taking behavior occurring in the target elevator.

In one possible implementation manner, the boarding behavior detection module includes: the target detection submodule is used for carrying out target detection on the video stream and determining a target to be tracked in the video stream; the characteristic extraction submodule is used for extracting the characteristics of the video stream to obtain the characteristic information to be detected corresponding to the target to be tracked; and the elevator taking behavior detection submodule is used for detecting elevator taking behaviors of the characteristic information to be detected according to at least one abnormal elevator taking behavior detection network and determining the visual detection result.

In one possible implementation, the apparatus further includes: a pre-judgment module; after the feature extraction module performs feature extraction on the video stream to obtain to-be-detected feature information corresponding to the to-be-tracked target, the pre-judgment module is configured to: determining the similarity between the characteristic information to be detected and at least one piece of prestored reference characteristic information, wherein one piece of reference characteristic information corresponds to an abnormal elevator riding behavior; and for any reference characteristic information, inputting the characteristic information to be detected into an abnormal elevator taking behavior detection network for detecting abnormal elevator taking behaviors corresponding to the reference characteristic information under the condition that the similarity between the characteristic information to be detected and the reference characteristic information is greater than a similarity threshold value.

In one possible implementation manner, the boarding behavior detection submodule is configured to: aiming at any abnormal elevator taking behavior detection network, the abnormal elevator taking behavior detection network is utilized to detect the elevator taking behavior of the characteristic information to be detected, and the prediction probability of the abnormal elevator taking behavior corresponding to the abnormal elevator taking behavior detection network in the target elevator is obtained; and determining that the abnormal elevator taking behaviors corresponding to the abnormal elevator taking behavior detection network occur in the target elevator under the condition that the prediction probability is greater than or equal to the probability threshold corresponding to the abnormal elevator taking behavior detection network.

In a possible implementation manner, the device information obtaining module is configured to: determining the behavior type of each abnormal elevator riding behavior; and acquiring the target equipment information corresponding to each abnormal elevator taking behavior according to the behavior type of each abnormal elevator taking behavior.

In a possible implementation manner, the verification module is configured to: aiming at any abnormal elevator taking behavior, determining whether the target elevator has an abnormal state corresponding to the abnormal elevator taking behavior according to the target equipment information corresponding to the abnormal elevator taking behavior; and determining the abnormal elevator taking behavior as the target abnormal elevator taking behavior when determining that the target elevator has an abnormal state corresponding to the abnormal elevator taking behavior.

In one possible implementation, the apparatus further includes: and the state adjusting module is used for adjusting the running state of the target elevator according to the behavior type of the abnormal elevator riding behavior of the target.

In one possible implementation, the apparatus further includes: the voice determining module is used for determining preset voice prompt information corresponding to the target abnormal elevator taking behavior according to the behavior type of the target abnormal elevator taking behavior; and the voice broadcasting control module is used for controlling the voice broadcasting device in the target elevator to broadcast the preset voice prompt information.

According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.

In the embodiment of the disclosure, the video stream of the target elevator is subjected to elevator riding behavior detection, a visual detection result for indicating whether abnormal elevator riding behaviors occur in the target elevator is determined, and under the condition that the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator, target equipment information of the target elevator is obtained to verify the at least one abnormal elevator riding behavior, so that a target detection result of the target abnormal elevator riding behavior occurring in the indicated target elevator can be determined, and the detection accuracy of the abnormal elevator riding behavior can be improved through double verification of detection and verification, thereby effectively reducing the probability of elevator accidents caused by the abnormal elevator riding behavior.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.

Fig. 1 shows a flow chart of a boarding behavior detection method according to an embodiment of the present disclosure;

fig. 2 shows a schematic diagram of an escalator ride detection system according to an embodiment of the present disclosure;

fig. 3 shows a block diagram of an escalator ride detection arrangement according to an embodiment of the disclosure;

FIG. 4 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure;

fig. 5 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

According to statistics, one third of elevator accidents are caused by non-civilized behaviors, such as actions of beating frames in an elevator, randomly knocking and colliding an elevator door by people or an electric vehicle, jumping in the elevator and the like to cause elevator vibration, actions of repeatedly stopping the elevator door by hands and objects and the like. In the related technology, real-time video data of a camera in an elevator is processed through a neural network model and used for judging whether abnormal behaviors occur in a video picture. However, on the one hand, since there is less video data including abnormal elevator riding behaviors and the number of training sets is limited, the detection accuracy of the neural network for detecting abnormal elevator riding behaviors is low. On the other hand, the abnormal elevator riding behaviors are various in types, and the difficulty in judging the abnormal elevator riding behaviors is increased due to the narrow space in the elevator. Therefore, the accuracy of detecting abnormal elevator riding behavior cannot be well guaranteed through single visual detection.

The method for detecting the elevator riding behaviors can be applied to scenes such as communities, markets, office buildings, public transportation and the like, the elevator riding behaviors in the scenes are detected through the video stream of the target elevator, the visual detection result for indicating whether the abnormal elevator riding behaviors occur in the target elevator is determined, and the target equipment information of the target elevator is obtained to carry out double verification on the visual detection result under the condition that the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator, so that the detection accuracy of the abnormal elevator riding behaviors can be improved, and the target detection result with higher accuracy is obtained. And under the condition that the target detection result indicates that the target abnormal elevator taking behavior occurs in the target elevator, timely giving an alarm and performing corresponding processing on the target elevator, for example, adjusting the running state of the target elevator and the like, so that the probability of elevator accidents caused by the abnormal elevator taking behavior can be effectively reduced.

Fig. 1 shows a flow chart of a boarding behavior detection method according to an embodiment of the present disclosure. The elevator riding detection method may be performed by an electronic device such as a terminal device or a server, where the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, and the like, and the elevator riding detection method may be implemented by a processor calling a computer-readable instruction stored in a memory. Alternatively, the boarding behavior detection method may be executed by a server. As shown in fig. 1, the elevator boarding behavior detection method may include:

in step S11, a video stream of the target elevator is acquired.

In step S12, elevator riding behavior detection is performed based on the video stream, and a visual detection result indicating whether abnormal elevator riding behavior occurs in the target elevator is determined.

In step S13, when the visual detection result indicates that at least one abnormal riding behavior has occurred in the target elevator, target device information of the target elevator is acquired.

In step S14, at least one abnormal riding behavior is verified based on the target device information, and a target detection result indicating the target abnormal riding behavior occurring in the target elevator is determined.

The abnormal elevator riding behavior may include: actions such as putting up a frame in the elevator, randomly knocking and disorderly hitting an elevator door by people or an electric vehicle, jumping in the elevator and the like, repeatedly blocking the elevator door from closing by hands and objects and the like, and also can comprise other civilized actions which may influence the normal operation of the elevator or may cause the occurrence of an elevator accident.

In one possible implementation, at least one image acquisition device, for example, a camera, is installed in the target elevator. Based on at least one image acquisition device installed in the target elevator, video acquisition can be carried out on the target elevator, so that the video stream of the target elevator is acquired. The installation position, the number, the video acquisition mode and the like of the image acquisition equipment in the target elevator can be set according to the actual situation, and the disclosure is not particularly limited to this.

After the video stream of the target elevator is obtained, the elevator taking behavior of the video stream can be detected, so that a visual detection result for indicating whether the abnormal elevator taking behavior occurs in the target elevator is obtained.

In one possible implementation manner, performing elevator riding behavior detection according to a video stream, and determining a visual detection result includes: carrying out target detection on the video stream, and determining a target to be tracked in the video stream; extracting the characteristics of the video stream to obtain the characteristic information to be detected corresponding to the target to be tracked; and detecting the elevator taking behavior of the characteristic information to be detected according to at least one abnormal elevator taking behavior detection network, and determining a visual detection result.

Decoding the video stream, carrying out target detection on the decoded video stream, and determining a detection frame in each video frame of the video stream; and tracking the detection frames in each video frame, and determining the detection frames belonging to the same target to be tracked so as to realize the tracking detection of the same target to be tracked. The target to be tracked may be a passenger taking a target elevator, may be an illegal item (e.g., an electric car) carried by the passenger, and may also be another target where abnormal elevator riding behavior may occur, which is not specifically limited by the present disclosure.

The target detection mode can adopt feature point detection, human body key point identification, human body contour detection and the like. The method for tracking the detection frames in each video frame may be, for example, performing an intersection ratio on the detection frames in adjacent video frames, and determining the detection frames belonging to the same target to be tracked according to the intersection ratio. It will be appreciated by those skilled in the art that target detection and tracking can be achieved in any manner known in the relevant art, and the present disclosure is not limited thereto.

For the same target to be tracked, feature extraction is performed on the video stream, for example, feature extraction is performed on an image area corresponding to a detection frame of the target to be tracked in each video frame of the video stream, so as to obtain feature information to be detected corresponding to the target to be tracked. According to the characteristic information to be detected, the behavior amplitude, the body action and the like of the target to be tracked can be tracked and detected, so that whether abnormal elevator riding behaviors occur to the target to be tracked can be judged in advance.

In a possible implementation manner, after performing feature extraction on a video stream to obtain feature information to be detected corresponding to a target to be tracked, the elevator riding behavior detection method further includes: determining the similarity between the characteristic information to be detected and at least one kind of prestored reference characteristic information, wherein one kind of reference characteristic information corresponds to an abnormal elevator riding behavior; and for any reference characteristic information, inputting the characteristic information to be detected into an abnormal elevator taking behavior detection network for detecting abnormal elevator taking behaviors corresponding to the reference characteristic information under the condition that the similarity between the characteristic information to be detected and the reference characteristic information is greater than a similarity threshold value.

In one example, similarity calculation is performed on to-be-detected feature information corresponding to a to-be-tracked target and at least one type of pre-stored reference feature information, and similarity between the to-be-detected feature information and each reference feature information is determined. The reference feature information corresponds to an abnormal elevator riding behavior, and a specific form of the reference feature information may be determined according to an actual situation, for example, the reference feature information may be an image feature obtained by performing feature extraction on an image in which the abnormal elevator riding behavior occurs, or may be an image itself corresponding to the abnormal elevator riding behavior, which is not specifically limited by the present disclosure.

And under the condition that the similarity between the characteristic information to be detected and certain reference characteristic information is greater than a similarity threshold value, indicating that the pre-judgment result is that the target to be tracked tends to have abnormal elevator riding behavior corresponding to the reference characteristic information. The specific value of the similarity threshold may be determined according to actual conditions, and is not specifically limited by the present disclosure.

In an example, behavior pre-judgment can be performed on the characteristic information to be detected corresponding to the target to be tracked through a pre-trained convolutional neural network, so as to obtain a pre-judgment result for indicating whether the target to be tracked tends to have some abnormal stair-riding behavior. The specific form and training process of the convolutional neural network may adopt any network form and training mode in the related art, and the disclosure does not specifically limit this.

According to the pre-judgment result, under the condition that the target to be tracked tends to have a certain abnormal elevator taking behavior, the abnormal elevator taking behavior detection network for detecting the abnormal elevator taking behavior can be accurately selected, and the characteristic information to be detected corresponding to the target to be tracked is input into the abnormal elevator taking behavior detection network, so that whether the abnormal elevator taking behavior occurs to the target to be tracked is further accurately detected.

According to the pre-judgment result, under the condition that the target to be tracked tends to have various abnormal elevator taking behaviors, a plurality of abnormal elevator taking behavior detection networks for detecting the various abnormal elevator taking behaviors can be accurately selected. And inputting the characteristic information to be detected corresponding to the target to be tracked into the abnormal elevator taking behavior detection networks so as to further accurately detect whether the target to be tracked has the various abnormal elevator taking behaviors.

In an example, the feature information to be detected corresponding to the target to be tracked can be directly input into each abnormal elevator taking behavior detection network trained in advance, so that accurate detection of various abnormal elevator taking behaviors is realized.

The abnormal elevator taking behavior detection network is trained in advance based on an abnormal elevator taking behavior data set, and the abnormal elevator taking behavior data set can comprise training data corresponding to various abnormal elevator taking behaviors. An abnormal elevator taking behavior detection network is used for detecting an abnormal elevator taking behavior. The specific form and training process of the abnormal elevator taking behavior detection network may adopt any network form and training mode in the related technology, and the specific form and training process of different abnormal elevator taking behavior detection networks may be the same or different, and the disclosure does not specifically limit this.

The elevator taking behavior detection network may be a convolutional neural network, or may be a neural network in another form that can be used for elevator taking behavior detection, and the specific form and training process of the elevator taking behavior detection network may adopt any network form and training mode in the related art, which is not specifically limited in this disclosure.

Inputting the characteristic information to be detected corresponding to the target to be tracked into a certain abnormal elevator taking behavior detection network, for example, inputting a fighting elevator taking behavior detection network, wherein the fighting elevator taking behavior detection network is a neural network for detecting the abnormal elevator taking behavior of fighting. The detection network of the behavior of getting up the frame detects the behavior of getting up the frame to the characteristic information to be detected, obtains the prediction probability of the abnormal behavior of getting up the frame in the target elevator, and can determine the abnormal behavior of getting up the frame in the target elevator under the condition that the prediction probability is greater than or equal to the probability threshold value corresponding to the detection network of the behavior of getting up the frame.

For example, the probability threshold corresponding to the detection network for the racking and boarding behaviors is 0.9, and when the detection network for the racking and boarding behaviors detects the characteristic information to be detected and obtains the prediction probability of the abnormal boarding behaviors, such as racking and boarding, occurring in the target elevator is 0.95, the abnormal boarding behaviors, such as racking and boarding, occurring in the target elevator can be determined.

The probability thresholds corresponding to different abnormal elevator riding behavior detection networks may be the same or different, and the specific value may be determined according to the actual situation, which is not specifically limited by the present disclosure.

When the video stream of the target elevator is detected, and the obtained visual detection result indicates that at least one abnormal behavior occurs in the target elevator, in order to improve the detection accuracy of the abnormal elevator riding behavior, the visual detection result can be further verified by using the equipment information of the target elevator under the condition that the target elevator is in a normal operation mode.

In one possible implementation manner, in a case where the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator, the obtaining of the target device information of the target elevator includes: determining the behavior type of each abnormal elevator riding behavior; and acquiring target equipment information corresponding to each abnormal elevator taking behavior according to the behavior type of each abnormal elevator taking behavior.

And under the condition that the target elevator is determined to be in a normal operation mode by acquiring the operation state information of the target elevator, acquiring target equipment information corresponding to each abnormal elevator riding behavior according to the behavior type of each abnormal elevator riding behavior indicated by the visual detection result.

For example, under the condition that the visual detection result indicates that the abnormal elevator taking behavior of repeatedly blocking the closing of the elevator door occurs in the target elevator, the elevator door state information of the target elevator is obtained; for example, when the visual detection result indicates that an abnormal riding behavior of a type of overhead climbing occurs in the target elevator, acceleration detection information, gyro sensor detection information, and the like of the target elevator are acquired. The mapping relationship of the target device information corresponding to different abnormal elevator riding behaviors can be preset according to actual conditions, and the mapping relationship is not particularly limited by the disclosure.

According to the behavior type of the abnormal elevator riding behavior in the target elevator indicated by the visual detection result, the target equipment information corresponding to the abnormal elevator riding behavior is obtained, the verification range of the equipment information of the target elevator can be narrowed, and the verification speed is improved.

In one possible implementation manner, verifying at least one abnormal elevator riding behavior according to the target device information, and determining a target detection result includes: aiming at any abnormal elevator taking behavior, determining whether the target elevator has an abnormal state corresponding to the abnormal elevator taking behavior according to the target equipment information corresponding to the abnormal elevator taking behavior; and when the target elevator is determined to have an abnormal state corresponding to the abnormal elevator riding behavior, determining the abnormal elevator riding behavior as the target abnormal elevator riding behavior.

And aiming at the abnormal elevator taking behavior which is indicated by the visual detection result and appears in the target elevator, determining whether the target elevator has an abnormal state corresponding to the abnormal elevator taking behavior or not according to the target equipment information corresponding to the abnormal elevator taking behavior. For example, if the visual detection result indicates that an abnormal riding behavior such as a landing operation occurs in the target elevator, it is determined whether the target elevator is in an abnormal speed state or not based on the acceleration detection information of the target elevator, or it is determined whether the target elevator is in an abnormal inclined state or not based on the detection information of the gyro sensor of the target elevator and whether the target elevator is in an abnormal stop state such as a stop state outside the unlocking area or not.

When the target elevator is determined to have an abnormal state corresponding to the abnormal elevator riding behavior indicated by the visual detection result, the abnormal elevator riding behavior can be determined as the target abnormal elevator riding behavior, namely, the target abnormal elevator riding behavior in the target elevator is determined. The target abnormal elevator riding behavior is obtained by verifying the authenticity of the visual detection result by using the equipment information, so that the detection accuracy of the target abnormal elevator riding behavior is effectively improved.

In one possible implementation manner, the boarding behavior detection method further includes: and adjusting the running state of the target elevator according to the behavior type of the abnormal elevator riding behavior of the target.

In the case that the target abnormal elevator riding behavior occurs in the target elevator, the operation state of the elevator can be adjusted according to the behavior type of the target abnormal elevator riding behavior, for example, the operation speed of the target elevator is reduced, or the staying time of the elevator door of the target elevator is adjusted, so as to reduce the probability of the occurrence of the elevator accident caused by the target abnormal elevator riding behavior.

In one possible implementation manner, the boarding behavior detection method further includes: determining preset voice prompt information corresponding to the target abnormal elevator taking behavior according to the behavior type of the target abnormal elevator taking behavior; and controlling a voice broadcasting device in the target elevator to broadcast preset voice prompt information.

Under the condition that the target abnormal elevator riding behavior is determined to occur in the target elevator, the preset voice prompt information corresponding to the target abnormal elevator riding behavior, such as 'please stop to hit the door', can be determined according to the behavior type of the target abnormal elevator riding behavior, and the voice broadcasting device in the target elevator is controlled to broadcast the preset voice prompt information so as to remind passengers in the target elevator to stop the target abnormal elevator riding behavior, so that the probability of elevator accidents caused by the target abnormal elevator riding behavior is reduced.

The preset voice prompt information corresponding to different abnormal elevator riding behaviors can be preset, and the preset voice prompt information is not specifically limited by the disclosure. The voice broadcasting device in the target elevator can be an image acquisition device with a voice broadcasting function in the target elevator or other voice broadcasting devices installed in the target elevator, and the disclosure is not particularly limited to this.

Fig. 2 shows a schematic diagram of an elevator boarding behavior detection system according to an embodiment of the present disclosure. As shown in fig. 2, the elevator riding behavior detection system comprises:

in step S21, a video stream of the target elevator is acquired.

In step S22, the video stream is preprocessed.

Wherein, the pretreatment process comprises the following steps: carrying out target detection on the video stream, and determining a target to be tracked in the video stream; and extracting the characteristics of the video stream to obtain the characteristic information to be detected corresponding to the target to be tracked.

In step S23, the pre-processed video stream is subjected to elevator boarding detection, and a visual detection result indicating whether or not an abnormal elevator boarding behavior occurs in the target elevator is determined.

Skipping to execute step S24 when the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator; otherwise, the jump is performed to step S21.

In step S24, target device information corresponding to the abnormal boarding behavior is acquired based on the behavior type of the abnormal boarding behavior indicated by the visual detection result.

In step S25, the abnormal riding behavior indicated by the visual detection result is checked based on the target device information, and it is determined whether or not the target abnormal riding behavior occurs in the target elevator.

When the target abnormal elevator riding behavior is determined to occur in the target elevator, jumping to execute the step S26 and the step S27; otherwise, the jump is performed to step S21.

In step S26, the operation state of the target elevator is adjusted according to the behavior type of the target abnormal riding behavior.

In step S27, determining preset voice prompt information corresponding to the target abnormal boarding behavior according to the behavior type of the target abnormal boarding behavior; and controlling a voice broadcasting device in the target elevator to broadcast preset voice prompt information.

The execution sequence of steps S26 and S27 is not particularly limited.

For specific details in the detection process of the elevator boarding behavior detection system, reference may be made to relevant contents in the embodiment shown in fig. 1, and details are not described here.

It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.

In addition, the present disclosure also provides an elevator taking behavior detection device, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the elevator taking behavior detection methods provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are omitted for brevity.

Fig. 3 shows a block diagram of an elevator boarding behavior detection apparatus according to an embodiment of the present disclosure. As shown in fig. 3, the boarding behavior detection device 30 includes:

a video stream acquiring module 31, configured to acquire a video stream of the target elevator;

the elevator taking behavior detection module 32 is configured to perform elevator taking behavior detection according to the video stream, and determine a visual detection result, where the visual detection result is used to indicate whether an abnormal elevator taking behavior occurs in the target elevator;

the equipment information acquisition module 33 is used for acquiring target equipment information of the target elevator under the condition that the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator;

and the checking module 34 is used for checking at least one abnormal elevator taking behavior according to the target device information and determining a target detection result, wherein the target detection result is used for indicating the target abnormal elevator taking behavior occurring in the target elevator.

In one possible implementation, the boarding behavior detection module 32 includes:

the target detection submodule is used for carrying out target detection on the video stream and determining a target to be tracked in the video stream;

the characteristic extraction submodule is used for extracting the characteristics of the video stream to obtain the characteristic information to be detected corresponding to the target to be tracked;

and the elevator taking behavior detection submodule is used for detecting elevator taking behaviors to the characteristic information to be detected according to at least one abnormal elevator taking behavior detection network and determining a visual detection result.

In one possible implementation manner, the boarding behavior detection device 30 further includes: a pre-judgment module;

after the feature extraction submodule performs feature extraction on the video stream to obtain to-be-detected feature information corresponding to the to-be-tracked target, the prejudging module is used for:

determining the similarity between the characteristic information to be detected and at least one kind of prestored reference characteristic information, wherein one kind of reference characteristic information corresponds to an abnormal elevator riding behavior;

and for any reference characteristic information, inputting the characteristic information to be detected into an abnormal elevator taking behavior detection network for detecting abnormal elevator taking behaviors corresponding to the reference characteristic information under the condition that the similarity between the characteristic information to be detected and the reference characteristic information is greater than a similarity threshold value.

In one possible implementation, the boarding behavior detection submodule 32 is configured to:

aiming at any abnormal elevator taking behavior detection network, the abnormal elevator taking behavior detection network is utilized to detect the elevator taking behavior of the characteristic information to be detected, and the prediction probability of the abnormal elevator taking behavior corresponding to the abnormal elevator taking behavior detection network in the target elevator is obtained;

and determining the abnormal elevator taking behaviors corresponding to the abnormal elevator taking behavior detection network in the target elevator when the prediction probability is greater than or equal to the probability threshold corresponding to the abnormal elevator taking behavior detection network.

In a possible implementation manner, the device information obtaining module 33 is configured to:

determining the behavior type of each abnormal elevator riding behavior;

and acquiring target equipment information corresponding to each abnormal elevator taking behavior according to the behavior type of each abnormal elevator taking behavior.

In one possible implementation, the checking module 34 is configured to:

aiming at any abnormal elevator taking behavior, determining whether the target elevator has an abnormal state corresponding to the abnormal elevator taking behavior according to the target equipment information corresponding to the abnormal elevator taking behavior;

and under the condition that the target elevator is determined to have an abnormal state corresponding to the abnormal elevator taking behavior, determining the abnormal elevator taking behavior as the target abnormal elevator taking behavior.

In one possible implementation manner, the boarding behavior detection device 30 further includes:

and the state adjusting module is used for adjusting the running state of the target elevator according to the behavior type of the abnormal elevator riding behavior of the target.

the voice determination module is used for determining preset voice prompt information corresponding to the target abnormal elevator taking behavior according to the behavior type of the target abnormal elevator taking behavior;

and the voice broadcasting control module is used for controlling the voice broadcasting device in the target elevator to broadcast preset voice prompt information.

In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.

Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a volatile or non-volatile computer readable storage medium.

An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

The disclosed embodiments also provide a computer program product comprising computer readable code or a non-transitory computer readable storage medium carrying computer readable code, which when run in a processor of an electronic device, the processor in the electronic device performs the above method.

The electronic device may be provided as a terminal, server, or other form of device.

Fig. 4 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure. As shown in fig. 4, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.

Referring to fig. 4, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.

The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a Complementary Metal Oxide Semiconductor (CMOS) or Charge Coupled Device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as a wireless network (WiFi), a second generation mobile communication technology (2G) or a third generation mobile communication technology (3G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.

Fig. 5 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure. As shown in fig. 5, the electronic device 1900 may be provided as a server. Referring to fig. 5, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.

The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900,a wired or wireless network interface 1950 is configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system, such as the Microsoft Server operating system (Windows Server), stored in the memory 1932^TM) Apple Inc. of the present application based on the graphic user interface operating System (Mac OS X)^TM) Multi-user, multi-process computer operating system (Unix)^TM) Free and open native code Unix-like operating System (Linux)^TM) Open native code Unix-like operating System (FreeBSD)^TM) Or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for detecting an elevator riding behavior, comprising:

acquiring a video stream of a target elevator;

detecting elevator riding behaviors according to the video stream, and determining a visual detection result, wherein the visual detection result is used for indicating whether abnormal elevator riding behaviors occur in the target elevator;

under the condition that the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator, target equipment information of the target elevator is obtained;

and verifying the at least one abnormal elevator taking behavior according to the target equipment information, and determining a target detection result, wherein the target detection result is used for indicating the target abnormal elevator taking behavior occurring in the target elevator.

2. The method of claim 1, wherein the performing the elevator riding behavior detection according to the video stream and determining the visual detection result comprises:

carrying out target detection on the video stream, and determining a target to be tracked in the video stream;

extracting the characteristics of the video stream to obtain the characteristic information to be detected corresponding to the target to be tracked;

and detecting the elevator taking behavior of the characteristic information to be detected according to at least one abnormal elevator taking behavior detection network, and determining the visual detection result.

3. The method according to claim 2, wherein after performing feature extraction on the video stream to obtain feature information to be detected corresponding to the target to be tracked, the method further comprises:

determining the similarity between the characteristic information to be detected and at least one piece of prestored reference characteristic information, wherein one piece of reference characteristic information corresponds to an abnormal elevator riding behavior;

4. The method according to claim 2 or 3, wherein the detecting the elevator-taking behavior of the feature information to be detected according to at least one abnormal elevator-taking behavior detecting network, and determining the visual detection result comprises:

and determining that the abnormal elevator taking behaviors corresponding to the abnormal elevator taking behavior detection network occur in the target elevator under the condition that the prediction probability is greater than or equal to the probability threshold corresponding to the abnormal elevator taking behavior detection network.

5. The method according to any one of claims 1 to 4, wherein the obtaining target equipment information of the target elevator in the case that the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator comprises:

determining the behavior type of each abnormal elevator riding behavior;

and acquiring the target equipment information corresponding to each abnormal elevator taking behavior according to the behavior type of each abnormal elevator taking behavior.

6. The method according to claim 5, wherein the verifying the at least one abnormal boarding action according to the target device information to determine a target detection result comprises:

and determining the abnormal elevator taking behavior as the target abnormal elevator taking behavior when determining that the target elevator has an abnormal state corresponding to the abnormal elevator taking behavior.

7. The method according to any one of claims 1 to 6, further comprising:

and adjusting the running state of the target elevator according to the behavior type of the target abnormal elevator riding behavior.

8. The method according to any one of claims 1 to 7, further comprising:

determining preset voice prompt information corresponding to the target abnormal elevator taking behavior according to the behavior type of the target abnormal elevator taking behavior;

and controlling a voice broadcasting device in the target elevator to broadcast the preset voice prompt information.

9. An elevator riding behavior detection device, comprising:

the video stream acquisition module is used for acquiring the video stream of the target elevator;

the elevator taking behavior detection module is used for detecting elevator taking behaviors according to the video stream and determining a visual detection result, wherein the visual detection result is used for indicating whether abnormal elevator taking behaviors occur in the target elevator;

the equipment information acquisition module is used for acquiring target equipment information of the target elevator under the condition that the visual detection result indicates that at least one abnormal elevator riding behavior occurs in the target elevator;

and the checking module is used for checking the at least one abnormal elevator taking behavior according to the target equipment information and determining a target detection result, wherein the target detection result is used for indicating the target abnormal elevator taking behavior occurring in the target elevator.

10. The apparatus of claim 9, wherein the boarding behavior detection module comprises:

and the elevator taking behavior detection submodule is used for detecting elevator taking behaviors of the characteristic information to be detected according to at least one abnormal elevator taking behavior detection network and determining the visual detection result.

11. The apparatus of claim 10, further comprising: a pre-judgment module;

after the feature extraction submodule performs feature extraction on the video stream to obtain to-be-detected feature information corresponding to the to-be-tracked target, the prejudging module is configured to:

12. The apparatus according to claim 10 or 11, wherein the boarding behavior detection submodule is configured to:

13. The apparatus according to any one of claims 9 to 12, wherein the device information obtaining module is configured to:

determining the behavior type of each abnormal elevator riding behavior;

14. The apparatus of claim 13, wherein the verification module is configured to:

15. The apparatus of any one of claims 9 to 14, further comprising:

16. The apparatus of any one of claims 9 to 15, further comprising:

the voice determining module is used for determining preset voice prompt information corresponding to the target abnormal elevator taking behavior according to the behavior type of the target abnormal elevator taking behavior;

and the voice broadcasting control module is used for controlling the voice broadcasting device in the target elevator to broadcast the preset voice prompt information.

17. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to invoke the memory-stored instructions to perform the method of any one of claims 1 to 8.

18. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 8.