CN110853364A - Data monitoring method and device - Google Patents
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- CN110853364A CN110853364A CN201911130222.4A CN201911130222A CN110853364A CN 110853364 A CN110853364 A CN 110853364A CN 201911130222 A CN201911130222 A CN 201911130222A CN 110853364 A CN110853364 A CN 110853364A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
Abstract
The embodiment of the disclosure discloses a data monitoring method and device. One embodiment of the method comprises: acquiring a peripheral image of a target vehicle acquired by an image acquisition device according to a preset image acquisition period; inputting the peripheral image into a pre-constructed illegal behavior recognition model to obtain illegal behavior category information corresponding to the peripheral image, wherein the illegal behavior category information is used for indicating whether illegal behaviors exist in the peripheral image or not and indicating the categories of the illegal behaviors; and responding to the obtained illegal action category information to indicate that the illegal action exists in the peripheral image, and pushing the peripheral image and the illegal action category information corresponding to the peripheral image to the target monitoring terminal. The implementation mode is beneficial to realizing illegal behavior monitoring on the environmental data around the vehicle in time.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a data monitoring method and device.
Background
At present, most of traffic violations are captured by camera equipment fixed at an intersection or a certain road section. Illegal behaviors are difficult to capture on a road section without a camera. Some drivers sometimes take mobile phones to obtain evidence when encountering illegal behaviors, and some drivers can take evidence through looking back through a recorder, and the methods are time-consuming, troublesome and unsafe.
In the related art, there is a need for monitoring illegal behaviors of environmental data around a vehicle in time.
Disclosure of Invention
The embodiment of the disclosure provides a data monitoring method and device.
In a first aspect, an embodiment of the present disclosure provides a data monitoring method, including: acquiring a peripheral image of a target vehicle acquired by an image acquisition device according to a preset image acquisition period; inputting the peripheral image into a pre-constructed illegal behavior recognition model to obtain illegal behavior category information corresponding to the peripheral image, wherein the illegal behavior category information is used for indicating whether illegal behaviors exist in the peripheral image or not and indicating the categories of the illegal behaviors; and responding to the obtained illegal action category information to indicate that the illegal action exists in the peripheral image, and pushing the peripheral image and the illegal action category information corresponding to the peripheral image to the target monitoring terminal.
In some embodiments, the peripheral image acquired by the image acquisition device includes image acquisition time information; responding to the obtained illegal action category information to indicate that illegal actions exist in the peripheral image, pushing the peripheral image and the illegal action category information corresponding to the peripheral image to a target monitoring end, and the method comprises the following steps: responding to the obtained illegal action category information to indicate that illegal actions exist in the peripheral image, and storing the peripheral image serving as a target image into a target image set; screening target images meeting preset screening conditions from the target image set according to the image acquisition time information of each target image in the target image set; and determining relevant information of the screened target image based on the image acquisition time information of the screened target image, and pushing the screened target image, the relevant information of the screened target image and illegal behavior category information corresponding to the screened target image to a target monitoring terminal.
In some embodiments, the preset screening conditions include at least one of: the time difference between two adjacent target images in time indicated by the image acquisition time information is smaller than a preset time difference threshold; the target image comprises an image area representing the license plate, and the image area representing the license plate is provided with a preset number of characters.
In some embodiments, the related information includes image capture location information and license plate number information; and determining relevant information of the screened target image based on the image acquisition time information of the screened target image, and pushing the screened target image, the relevant information of the screened target image and illegal action category information corresponding to the screened target image to a target monitoring terminal, wherein the method comprises the following steps: traversing the screened target images, and executing the following steps when accessing the current target image: acquiring vehicle position information at a moment indicated by image acquisition time information of a current target image from navigation information of a target vehicle, and taking the acquired vehicle position information as image acquisition position information of the current target image; identifying license plate number information in a current target image; and sending the current target image, the image acquisition position information of the current target image, the license plate number information of the current target image and illegal behavior category information corresponding to the current target image to a target monitoring terminal.
In some embodiments, pushing the screened target images, the related information of the screened target images, and the illegal activity category information corresponding to the screened target images to the target monitoring end includes: pushing the screened target image and the illegal action category information corresponding to the screened target image to a target server to receive verification result information fed back by the target server aiming at the pushed content, wherein the verification result information is used for representing whether the category of the illegal action indicated by the illegal action category information corresponding to the screened target image is correct or not; and in response to the fact that the verification result information represents that the classification of the illegal action indicated by the illegal action classification information corresponding to the screened target image is correct, pushing the screened target image, the related information of the screened target image and the illegal action classification information corresponding to the screened target image to the target monitoring terminal.
In some embodiments, the illegal behavior recognition model is trained by: acquiring a training sample set, wherein the training sample comprises a vehicle periphery image and illegal behavior category information corresponding to the vehicle periphery image; and taking the vehicle periphery images of the training samples in the training sample set as input, taking the illegal behavior category information corresponding to the input vehicle periphery images as expected output, and training to obtain an illegal behavior recognition model.
In a second aspect, an embodiment of the present disclosure provides a data monitoring apparatus, including: an image acquisition unit configured to acquire a peripheral image of the target vehicle acquired by the image acquisition device according to a preset image acquisition cycle; a behavior recognition unit configured to input the surrounding image into a pre-constructed illegal behavior recognition model, and obtain illegal behavior category information corresponding to the surrounding image, the illegal behavior category information being used for indicating whether illegal behaviors exist in the surrounding image and categories of the illegal behaviors; and the data pushing unit is configured to respond to the obtained illegal behavior category information indicating that illegal behaviors exist in the peripheral image, and push the peripheral image and the illegal behavior category information corresponding to the peripheral image to the target monitoring terminal.
In some embodiments, the peripheral image acquired by the image acquisition device includes image acquisition time information; and the data pushing unit is further configured to: responding to the obtained illegal action category information to indicate that illegal actions exist in the peripheral image, and storing the peripheral image serving as a target image into a target image set; screening target images meeting preset screening conditions from the target image set according to the image acquisition time information of each target image in the target image set; and determining relevant information of the screened target image based on the image acquisition time information of the screened target image, and pushing the screened target image, the relevant information of the screened target image and illegal behavior category information corresponding to the screened target image to a target monitoring terminal.
In some embodiments, the preset screening conditions include at least one of: the time difference between two adjacent target images in time indicated by the image acquisition time information is smaller than a preset time difference threshold; the target image comprises an image area representing the license plate, and the image area representing the license plate is provided with a preset number of characters.
In some embodiments, the related information includes image capture location information and license plate number information; and determining relevant information of the screened target image based on the image acquisition time information of the screened target image, and pushing the screened target image, the relevant information of the screened target image and illegal action category information corresponding to the screened target image to a target monitoring terminal, wherein the method comprises the following steps: traversing the screened target images, and executing the following steps when accessing the current target image: acquiring vehicle position information at a moment indicated by image acquisition time information of a current target image from navigation information of a target vehicle, and taking the acquired vehicle position information as image acquisition position information of the current target image; identifying license plate number information in a current target image; and sending the current target image, the image acquisition position information of the current target image, the license plate number information of the current target image and illegal behavior category information corresponding to the current target image to a target monitoring terminal.
In some embodiments, pushing the screened target images, the related information of the screened target images, and the illegal activity category information corresponding to the screened target images to the target monitoring end includes: pushing the screened target image and the illegal action category information corresponding to the screened target image to a target server to receive verification result information fed back by the target server aiming at the pushed content, wherein the verification result information is used for representing whether the category of the illegal action indicated by the illegal action category information corresponding to the screened target image is correct or not; and in response to the fact that the verification result information represents that the classification of the illegal action indicated by the illegal action classification information corresponding to the screened target image is correct, pushing the screened target image, the related information of the screened target image and the illegal action classification information corresponding to the screened target image to the target monitoring terminal.
In some embodiments, the illegal behavior recognition model is trained by: acquiring a training sample set, wherein the training sample comprises a vehicle periphery image and illegal behavior category information corresponding to the vehicle periphery image; and taking the vehicle periphery images of the training samples in the training sample set as input, taking the illegal behavior category information corresponding to the input vehicle periphery images as expected output, and training to obtain an illegal behavior recognition model.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon; when executed by the one or more processors, cause the one or more processors to implement a method as described in any implementation of the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable medium on which a computer program is stored, which when executed by a processor implements the method as described in any of the implementations of the first aspect.
The data monitoring method and the data monitoring device provided by the embodiment of the disclosure can acquire the peripheral images of the target vehicle acquired by the image acquisition device according to the preset image acquisition period. Then, the surrounding image is input into a pre-constructed illegal behavior recognition model, and illegal behavior category information corresponding to the surrounding image is obtained, wherein the illegal behavior category information is used for indicating whether illegal behaviors exist in the surrounding image or not and indicating the categories of the illegal behaviors. And finally, responding to the obtained illegal action category information to indicate that the illegal action exists in the peripheral image, and pushing the peripheral image and the illegal action category information corresponding to the peripheral image to a target monitoring end. According to the method and the device provided by the embodiment of the disclosure, the illegal action analysis is carried out on the peripheral image of the target vehicle acquired by the image acquisition device, so that the peripheral image with the illegal action is pushed to the target monitoring terminal in time, and the illegal action monitoring is carried out on the environmental data around the vehicle in time.
Drawings
Other features, objects and advantages of the disclosure will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a flow diagram of one embodiment of a data monitoring method according to the present disclosure;
FIG. 2 is a flow diagram of another embodiment of a data monitoring method according to the present disclosure;
FIG. 3 is a schematic block diagram of one embodiment of a data monitoring device according to the present disclosure;
FIG. 4 is a schematic structural diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
FIG. 1 illustrates a flow 100 of one embodiment of a data monitoring method according to the present disclosure. The data monitoring method comprises the following steps:
In this embodiment, the execution subject of the data monitoring method may be an electronic device such as a data monitoring device. As an example, the execution subject may be a car recorder, a law enforcement recorder, or the like.
The image acquisition device may be a device with a camera function provided by the execution main body, or a device with a camera function in communication connection with the execution main body. The image acquisition device can comprise one camera or a plurality of cameras. It should be noted that, when the image capturing device includes a plurality of cameras, the distribution of the plurality of cameras may be set according to the actual application scenario. For example, it may be evenly distributed around the target vehicle.
The preset image capturing period may be a preset value for describing a time period, such as 1 second, 2 seconds, and the like. It should be noted that, since the duration of the illegal action of the vehicle is usually short during the running of the vehicle, in order to avoid the detection of the illegal action, the preset image capturing period is usually set to a small value, and is usually set to a unit of milliseconds, such as 30 milliseconds. In practice, the above-mentioned preset image capturing period may be set to 33 milliseconds. At this time, the image capturing device captures 30 frames of peripheral images per second.
The target vehicle is generally a vehicle in which a data monitoring apparatus as an execution subject is located.
In this embodiment, the image capturing device may capture a surrounding image of the target vehicle according to a preset image capturing period, and transmit the captured surrounding image to the execution main body that is communicatively connected thereto. In this way, the execution subject can acquire the peripheral image of the target vehicle.
And 102, inputting the surrounding image into a pre-constructed illegal behavior recognition model to obtain illegal behavior category information corresponding to the surrounding image.
The illegal action category information is used for indicating whether illegal actions exist in the peripheral image and the categories of the illegal actions. Here, the category of the illegal activity may include, but is not limited to, one of the following: the vehicle compaction line changes lanes, the roadside parks against the regulations, the vehicle runs without a specified lane, the vehicle runs the red light and the like.
The illegal behavior recognition model can be used for analyzing the corresponding relation between the peripheral image of the vehicle and the illegal behavior category information. Specifically, the illegal action recognition model may be a correspondence table that is generated based on statistics of surrounding images of a large number of vehicles and stores correspondence between surrounding images of a plurality of vehicles and illegal action type information, or may be a model obtained by training an initial model (for example, a Convolutional Neural Network (CNN), a residual error network (ResNet), or the like) by a machine learning method based on a training sample.
In some optional implementations of this embodiment, the illegal behavior recognition model may be obtained by training through the following steps: first, a set of training samples is obtained. The training sample comprises a vehicle periphery image and illegal behavior category information corresponding to the vehicle periphery image. The illegal activity category information may be used to indicate whether there is an illegal activity in the vehicle surrounding image and the category of the illegal activity. Then, the vehicle periphery images of the training samples in the training sample set are used as input, the illegal behavior category information corresponding to the input vehicle periphery images is used as expected output, and the illegal behavior recognition model is obtained through training.
The target monitoring terminal may be a designated terminal (such as a mobile phone) of a user of the target vehicle, a designated server of a traffic management department, or a designated terminal of the traffic management department.
In this embodiment, when the obtained illegal activity category information indicates that an illegal activity exists in the peripheral image, the execution main body may push the peripheral image and the illegal activity category information of the peripheral image to the target monitoring terminal in a network information or short message manner. It should be noted that, when the execution main body pushes a message to the target monitoring end, the execution main body generally pushes a timestamp of the message pushing time to the target monitoring end together. Therefore, the target monitoring end can search other information related to the surrounding image and at the same moment through the message pushing time, and accordingly the traffic illegal behaviors in the surrounding image can be tracked.
According to the method provided by the embodiment of the disclosure, the illegal action analysis is carried out on the peripheral image of the target vehicle acquired by the image acquisition device, so that the peripheral image with the illegal action is pushed to the target monitoring terminal in time, and the illegal action monitoring is carried out on the environmental data around the vehicle in time.
In an alternative implementation of various embodiments of the present disclosure, the peripheral image acquired by the image acquisition device includes image acquisition time information. At this time, the pushing the surrounding image and the illegal activity category information corresponding to the surrounding image to the target monitoring end in response to the obtained illegal activity category information indicating that the illegal activity exists in the surrounding image includes:
and step one, in response to the fact that the obtained illegal action type information indicates that illegal actions exist in the peripheral image, the peripheral image is taken as a target image and is stored into a target image set.
Here, when the obtained illegal action category information indicates that there is an illegal action in the surrounding image, the execution subject may store the surrounding image in the target image set. It should be noted that, by default, the target image set is an empty set.
And secondly, screening out target images meeting preset screening conditions from the target image set according to the image acquisition time information of each target image in the target image set.
Here, when the target image set satisfies the preset batch processing condition, the execution subject may filter the target images in the target image set according to the image capturing time information of each target image, so as to obtain the target images satisfying the preset filtering condition.
The preset batch processing condition may be a preset condition for triggering processing of the target image set. As an example, the preset batch processing condition may be that the number of target images in the target image set reaches a set value, such as 100, or that the difference between the storage time of the first stored target image and the storage time of the last stored target image in the target image set reaches a set difference, such as 3 minutes. In addition, the preset filtering condition may be a preset filtering condition for filtering the target image from the target image set. As an example, the preset filtering condition may be to randomly filter a set number (e.g., 20) of target images.
Optionally, the preset screening condition may include, but is not limited to, at least one of the following: the first condition is as follows: and the time difference between two adjacent target images in time indicated by the image acquisition time information is smaller than a preset time difference threshold value. And a second condition: the target image comprises an image area representing the license plate, and the image area representing the license plate is provided with a preset number of characters.
The preset time difference threshold may be a preset time difference value. In practice, the value of the preset time difference threshold is usually small because the duration of the vehicle illegal action is usually short in the vehicle driving process, and in practice, the preset time difference threshold is usually set to be close to the preset image acquisition period and slightly larger than the preset image acquisition period. For example, if the preset image capturing period is 33 milliseconds, the preset time difference threshold may be set to 34 milliseconds, 40 milliseconds, or the like. In addition, when vehicle illegal behaviors exist, the vehicle illegal behaviors are usually reflected in continuous multi-frame images. Therefore, the executing body may further determine whether there is illegal action by analyzing a magnitude relation between a time difference between a plurality of target images adjacent to the image capturing time and a preset time difference threshold. Therefore, when the preset screening condition is the condition one, misjudgment of the traffic illegal action can be avoided, and the accuracy of monitoring the illegal action on the environmental data around the vehicle is improved.
In addition, the predetermined number may be a predetermined number. The length of the license plate number of the vehicle is usually fixed and is 7 characters, such as Beijing A12345. Therefore, it is common practice to set the above-mentioned preset number to 7. It should be noted that when the preset screening condition is the second condition, the target image capable of accurately identifying the license plate number can be screened from the target image set. Thus, the effectiveness and the practicability of illegal behavior monitoring on the environmental data around the vehicle are improved.
It should be noted that, in practice, the screening may be performed first under the condition one, and then under the condition two. Thus, the effectiveness and the practicability of illegal behavior monitoring on the environmental data around the vehicle are further improved.
And thirdly, determining relevant information of the screened target image based on the image acquisition time information of the screened target image, and pushing the screened target image, the relevant information of the screened target image and illegal action category information corresponding to the screened target image to a target monitoring terminal.
Here, the execution subject may acquire other information at the same time as the image acquisition time using the image acquisition time of the screened target image, and use the acquired other information as the related information of the target image. As an example, the other information may include, but is not limited to, position information at the time, a surrounding image of the other vehicle at a position indicated by the position information at the time, and the like.
After obtaining the relevant information of the screened target image, the execution subject may push the relevant information of the target image, the illegal action category information, and the target image to the target monitoring terminal. Therefore, the target monitoring terminal can acquire more information related to the illegal behaviors of the vehicles, and can accurately trace the vehicles related to the illegal behaviors of the traffic, so that the practicability of monitoring the illegal behaviors of the environmental data around the vehicles is further improved.
In the foregoing implementation manner, the related information may include image capture position information and license plate number information. At this time, the determining, based on the image capturing time information of the screened target image, the relevant information of the screened target image, and pushing the screened target image, the relevant information of the screened target image, and the illegal activity category information corresponding to the screened target image to the target monitoring end includes:
traversing the screened target images, and executing the following steps when accessing the current target image:
step one, vehicle position information of the time indicated by the image acquisition time information of the current target image is acquired from the navigation information of the target vehicle, and the acquired vehicle position information is used as the image acquisition position information of the current target image.
Here, there may be one or a plurality of target images selected from the target image set. The execution subject may execute steps one to three for each of the screened target images. Since the vehicle is usually installed with a navigation system, for a currently accessed target image, that is, a current target image, the execution subject may obtain vehicle position information at the same time as the image capturing time of the current target image from navigation information of the navigation system of the target vehicle, so as to obtain image capturing position information of the current target image.
And step two, identifying license plate number information in the current target image.
Here, the execution subject may recognize license plate number information in the current target image using a license plate recognition algorithm.
And step three, sending the current target image, the image acquisition position information of the current target image, the license plate number information of the current target image and the illegal action category information corresponding to the current target image to a target monitoring terminal.
Here, the execution subject may send the current target image, and image capture position information, license plate number information, illegal action category information, and the current target image of the current target image to the target monitoring terminal together. Therefore, vehicles related to traffic illegal behaviors can be accurately tracked, and effectiveness and practicability of illegal behavior monitoring on environmental data around the vehicles are further improved.
With continued reference to FIG. 2, a flow 200 of another embodiment of a data monitoring method is shown. The process 200 of the data monitoring method includes the following steps:
The peripheral image acquired by the image acquisition device comprises image acquisition time information.
The illegal action category information is used for indicating whether illegal actions exist in the peripheral image and the categories of the illegal actions.
In the present embodiment, the specific operations of steps 201-202 are substantially the same as the operations of steps 101-102 in the embodiment shown in fig. 1, and are not repeated herein.
In this embodiment, the specific operation of step 203 is substantially the same as the operation of "responding to the obtained illegal behavior category information to indicate that there is illegal behavior in the surrounding image, using the surrounding image as the target image, and storing the target image into the target image set", and details are not repeated herein.
And 204, screening target images meeting preset screening conditions from the target image set according to the image acquisition time information of each target image in the target image set.
In this embodiment, the specific operation of step 204 is substantially the same as the aforementioned operation of "screening out the target image satisfying the preset screening condition from the target image set according to the image acquisition time information of each target image in the target image set", and is not described herein again.
In step 205, the screened target image and the illegal activity category information corresponding to the screened target image are pushed to the target server to receive the verification result information fed back by the target server for the pushed content.
And the verification result information is used for representing whether the classification of the illegal action indicated by the illegal action classification information corresponding to the screened target image is correct or not.
The target server may be a server with strong computing power. Such as a cloud server.
In this embodiment, the execution subject may push the screened target image and the illegal activity category information corresponding to the screened target image to the target server. In this way, after receiving the pushed content, the target server may perform secondary illegal behavior recognition on the screened target image by using a pre-trained illegal behavior recognition model on the target server. Specifically, if the two recognition results are the same, it is determined that the screened target image does indeed have an illegal action, and at this time, the target server may push, to the execution main body, verification result information indicating that the type of the illegal action indicated by the illegal action type information corresponding to the target image is correct. If not, it is determined that there may be no illegal activity, and at this time, the target server may push, to the execution main body, verification result information indicating that the type of the illegal activity indicated by the illegal activity type information corresponding to the target image is incorrect. In addition, when it is determined that there may be no illegal behavior, the target server may also push target images with different recognition results to a preset terminal for manual determination.
And step 206, in response to determining that the verification result information represents that the classification of the illegal action indicated by the illegal action classification information corresponding to the screened target image is correct, pushing the screened target image, the relevant information of the screened target image and the illegal action classification information corresponding to the screened target image to the target monitoring terminal.
In this embodiment, the execution main body may trigger execution to push the screened target image, the related information of the screened target image, and the illegal activity category information corresponding to the screened target image to the target monitoring terminal when receiving that the verification result information fed back by the target server represents that the category of the illegal activity indicated by the illegal activity category information corresponding to the screened target image is correct.
It should be noted that, as an alternative, the execution main body may also directly push the screened target image, the related information of the screened target image, and the illegal activity category information corresponding to the screened target image to the target server, so that the target server generates the verification result information for the pushed content, and when the verification result information indicates that the category of the illegal activity indicated by the illegal activity category information corresponding to the screened target image is correct, the target server pushes the screened target image, the related information of the screened target image, and the illegal activity category information corresponding to the screened target image to the target monitoring end.
In this embodiment, the target server is used to verify the illegal behavior recognition result of the execution main body, so that whether illegal behaviors exist in the surrounding image can be determined more accurately, and the accuracy of illegal behavior monitoring on the environmental data around the vehicle can be further improved. In addition, only the peripheral images actually having illegal behaviors are pushed to the target monitoring end, so that the consumption of network resources caused by unnecessary pushing operation can be reduced.
With further reference to fig. 3, as an implementation of the method shown in fig. 1, the present disclosure provides an embodiment of a data monitoring apparatus, which corresponds to the embodiment of the method shown in fig. 1, and which may be applied in various electronic devices.
As shown in fig. 3, the data monitoring apparatus 300 of the present embodiment includes: an image acquisition unit 301 configured to acquire a peripheral image of the target vehicle acquired by the image acquisition device according to a preset image acquisition cycle; a behavior recognition unit 302 configured to input the surrounding image into a pre-constructed illegal behavior recognition model, and obtain illegal behavior category information corresponding to the surrounding image, the illegal behavior category information being used for indicating whether illegal behaviors exist in the surrounding image and categories of the illegal behaviors; and the data pushing unit 303 is configured to, in response to that the obtained illegal activity category information indicates that an illegal activity exists in the peripheral image, push the peripheral image and the illegal activity category information corresponding to the peripheral image to the target monitoring terminal.
In some optional implementations of this embodiment, the peripheral image acquired by the image acquisition device includes image acquisition time information. At this time, the data pushing unit 303 may be further configured to: firstly, in response to the obtained illegal action category information indicating that the illegal action exists in the peripheral image, the peripheral image is taken as a target image and stored into a target image set. And then, screening out target images meeting preset screening conditions from the target image set according to the image acquisition time information of each target image in the target image set. And finally, determining relevant information of the screened target image based on the image acquisition time information of the screened target image, and pushing the screened target image, the relevant information of the screened target image and illegal behavior category information corresponding to the screened target image to a target monitoring terminal.
In some optional implementations of this embodiment, the preset screening condition includes at least one of: and the time difference between two adjacent target images in time indicated by the image acquisition time information is smaller than a preset time difference threshold value. The target image comprises an image area representing the license plate, and the image area representing the license plate is provided with a preset number of characters.
In some optional implementations of this embodiment, the related information includes image capture location information and license plate number information. At this time, determining relevant information of the screened target image based on the image capturing time information of the screened target image, and pushing the screened target image, the relevant information of the screened target image, and illegal activity category information corresponding to the screened target image to the target monitoring terminal may include: traversing the screened target images, and executing the following steps when accessing the current target image: first, vehicle position information at a time indicated by image capture time information of a current target image is acquired from navigation information of a target vehicle, and the acquired vehicle position information is used as image capture position information of the current target image. Then, the license plate number information in the current target image is identified. And finally, sending the current target image, the image acquisition position information of the current target image, the license plate number information of the current target image and the illegal action category information corresponding to the current target image to a target monitoring terminal.
In some optional implementation manners of this embodiment, pushing the screened target image, the related information of the screened target image, and the illegal activity category information corresponding to the screened target image to the target monitoring end may include: first, the screened target image and the illegal action category information corresponding to the screened target image are pushed to a target server to receive verification result information fed back by the target server for the pushed content. And the verification result information is used for representing whether the classification of the illegal action indicated by the illegal action classification information corresponding to the screened target image is correct or not. And in response to the fact that the verification result information represents that the classification of the illegal action indicated by the illegal action classification information corresponding to the screened target image is correct, pushing the screened target image, the related information of the screened target image and the illegal action classification information corresponding to the screened target image to the target monitoring terminal.
In some optional implementations of this embodiment, the illegal behavior recognition model is obtained by training through the following steps: first, a training sample set is obtained, the training sample including a vehicle periphery image and illicit behavior category information corresponding to the vehicle periphery image. Then, the vehicle periphery images of the training samples in the training sample set are used as input, the illegal behavior category information corresponding to the input vehicle periphery images is used as expected output, and the illegal behavior recognition model is obtained through training.
In the apparatus provided by the above embodiment of the present disclosure, the image obtaining unit 301 obtains the peripheral image of the target vehicle, which is obtained by the image capturing apparatus according to the preset image capturing period. Then, the behavior recognition unit 302 inputs the surrounding image into a pre-constructed illegal behavior recognition model, and obtains illegal behavior category information corresponding to the surrounding image, the illegal behavior category information being used to indicate whether there is an illegal behavior in the surrounding image and the category of the illegal behavior. Finally, the data pushing unit 303 responds to the obtained illegal activity category information indicating that the illegal activity exists in the peripheral image, and pushes the peripheral image and the illegal activity category information corresponding to the peripheral image to the target monitoring terminal. The device of this embodiment carries out illegal action analysis through the peripheral image to the target vehicle that the image acquisition device gathered, realizes in time will have the peripheral image propelling movement of illegal action to the target monitoring end, realizes in time carrying out illegal action control to the peripheral environmental data of vehicle.
Referring now to FIG. 4, a schematic diagram of an electronic device (e.g., data monitoring device) 400 suitable for use in implementing embodiments of the present disclosure is shown. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, the electronic device 400 may include a processing means (e.g., a Central Processing Unit (CPU), a graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage means 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 4 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 4 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 409, or from the storage device 408, or from the ROM 402. The computer program, when executed by the processing apparatus 401, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium of the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the steps of: acquiring a peripheral image of a target vehicle acquired by an image acquisition device according to a preset image acquisition period; inputting the peripheral image into a pre-constructed illegal behavior recognition model to obtain illegal behavior category information corresponding to the peripheral image, wherein the illegal behavior category information is used for indicating whether illegal behaviors exist in the peripheral image or not and indicating the categories of the illegal behaviors; and responding to the obtained illegal action category information to indicate that the illegal action exists in the peripheral image, and pushing the peripheral image and the illegal action category information corresponding to the peripheral image to the target monitoring terminal.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code 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).
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 code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, 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 units described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an image acquisition unit, a behavior recognition unit, and a data push unit. The names of these units do not in some cases constitute a limitation on the units themselves, and for example, the image acquisition unit may also be described as "acquiring a surrounding image of the target vehicle acquired by the image acquisition device in a preset image acquisition cycle".
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept as defined above. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (14)
1. A method of data monitoring, wherein the method comprises:
acquiring a peripheral image of a target vehicle acquired by an image acquisition device according to a preset image acquisition period;
inputting the peripheral image into a pre-constructed illegal behavior recognition model to obtain illegal behavior category information corresponding to the peripheral image, wherein the illegal behavior category information is used for indicating whether illegal behaviors exist in the peripheral image or not and indicating the categories of the illegal behaviors;
responding to the obtained illegal action category information to indicate that illegal actions exist in the peripheral image, and pushing the peripheral image and the illegal action category information corresponding to the peripheral image to a target monitoring end.
2. The method according to claim 1, wherein the peripheral image acquired by the image acquisition device includes image acquisition time information; and
the responding to the obtained illegal action category information indicating that the illegal action exists in the peripheral image, and pushing the peripheral image and the illegal action category information corresponding to the peripheral image to a target monitoring end comprises the following steps:
responding to the obtained illegal action category information to indicate that illegal actions exist in the peripheral image, and storing the peripheral image serving as a target image into a target image set;
screening target images meeting preset screening conditions from the target image set according to the image acquisition time information of each target image in the target image set;
and determining relevant information of the screened target image based on the image acquisition time information of the screened target image, and pushing the screened target image, the relevant information of the screened target image and illegal behavior category information corresponding to the screened target image to a target monitoring terminal.
3. The method of claim 2, wherein the preset screening conditions include at least one of:
the time difference between two adjacent target images in time indicated by the image acquisition time information is smaller than a preset time difference threshold;
the target image comprises an image area representing the license plate, and the image area representing the license plate is provided with a preset number of characters.
4. The method of claim 2, wherein the related information includes image capture location information and license plate number information; and
the image acquisition time information based on the screened target image, determining relevant information of the screened target image, and pushing the screened target image, the relevant information of the screened target image and illegal action category information corresponding to the screened target image to a target monitoring terminal, includes:
traversing the screened target images, and executing the following steps when accessing the current target image: acquiring vehicle position information at a moment indicated by the image acquisition time information of the current target image from the navigation information of the target vehicle, and taking the acquired vehicle position information as the image acquisition position information of the current target image; identifying license plate number information in a current target image; and sending the current target image, the image acquisition position information of the current target image, the license plate number information of the current target image and illegal behavior category information corresponding to the current target image to the target monitoring terminal.
5. The method according to one of claims 2 to 4, wherein the pushing the screened target images, the related information of the screened target images and the illegal activity category information corresponding to the screened target images to the target monitoring terminal comprises:
pushing the screened target image and illegal behavior category information corresponding to the screened target image to a target server to receive verification result information fed back by the target server aiming at the pushed content, wherein the verification result information is used for representing whether the category of illegal behavior indicated by the illegal behavior category information corresponding to the screened target image is correct or not;
in response to determining that the verification result information represents that the category of the illegal action indicated by the illegal action category information corresponding to the screened target image is correct, pushing the screened target image, the related information of the screened target image and the illegal action category information corresponding to the screened target image to the target monitoring terminal.
6. The method of claim 1, wherein the illegal behavior recognition model is trained by:
acquiring a training sample set, wherein the training sample comprises a vehicle periphery image and illegal behavior category information corresponding to the vehicle periphery image;
and taking the vehicle periphery images of the training samples in the training sample set as input, taking the illegal behavior category information corresponding to the input vehicle periphery images as expected output, and training to obtain the illegal behavior recognition model.
7. A data monitoring apparatus, wherein the apparatus comprises:
an image acquisition unit configured to acquire a peripheral image of the target vehicle acquired by the image acquisition device according to a preset image acquisition cycle;
a behavior recognition unit configured to input the surrounding image into a pre-constructed illegal behavior recognition model, and obtain illegal behavior category information corresponding to the surrounding image, wherein the illegal behavior category information is used for indicating whether illegal behaviors exist in the surrounding image or not and indicating the categories of the illegal behaviors;
and the data pushing unit is configured to respond to the obtained illegal behavior category information indicating that illegal behaviors exist in the peripheral image, and push the peripheral image and the illegal behavior category information corresponding to the peripheral image to a target monitoring terminal.
8. The apparatus according to claim 7, wherein the peripheral image acquired by the image acquisition apparatus includes image acquisition time information; and
the data pushing unit is further configured to:
responding to the obtained illegal action category information to indicate that illegal actions exist in the peripheral image, and storing the peripheral image serving as a target image into a target image set;
screening target images meeting preset screening conditions from the target image set according to the image acquisition time information of each target image in the target image set;
and determining relevant information of the screened target image based on the image acquisition time information of the screened target image, and pushing the screened target image, the relevant information of the screened target image and illegal behavior category information corresponding to the screened target image to a target monitoring terminal.
9. The apparatus of claim 8, wherein the preset screening condition comprises at least one of:
the time difference between two adjacent target images in time indicated by the image acquisition time information is smaller than a preset time difference threshold;
the target image comprises an image area representing the license plate, and the image area representing the license plate is provided with a preset number of characters.
10. The apparatus of claim 8, wherein the related information includes image capture location information and license plate number information; and
the image acquisition time information based on the screened target image, determining relevant information of the screened target image, and pushing the screened target image, the relevant information of the screened target image and illegal action category information corresponding to the screened target image to a target monitoring terminal, includes:
traversing the screened target images, and executing the following steps when accessing the current target image: acquiring vehicle position information at a moment indicated by the image acquisition time information of the current target image from the navigation information of the target vehicle, and taking the acquired vehicle position information as the image acquisition position information of the current target image; identifying license plate number information in a current target image; and sending the current target image, the image acquisition position information of the current target image, the license plate number information of the current target image and illegal behavior category information corresponding to the current target image to the target monitoring terminal.
11. The apparatus according to one of claims 8 to 10, wherein the pushing the screened target images, the related information of the screened target images, and the illegal activity category information corresponding to the screened target images to the target monitoring terminal includes:
pushing the screened target image and illegal behavior category information corresponding to the screened target image to a target server to receive verification result information fed back by the target server aiming at the pushed content, wherein the verification result information is used for representing whether the category of illegal behavior indicated by the illegal behavior category information corresponding to the screened target image is correct or not;
in response to determining that the verification result information represents that the category of the illegal action indicated by the illegal action category information corresponding to the screened target image is correct, pushing the screened target image, the related information of the screened target image and the illegal action category information corresponding to the screened target image to the target monitoring terminal.
12. The apparatus of claim 7, wherein the illegal behavior recognition model is trained by:
acquiring a training sample set, wherein the training sample comprises a vehicle periphery image and illegal behavior category information corresponding to the vehicle periphery image;
and taking the vehicle periphery images of the training samples in the training sample set as input, taking the illegal behavior category information corresponding to the input vehicle periphery images as expected output, and training to obtain the illegal behavior recognition model.
13. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
14. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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