CN110895662A - Vehicle overload alarm method and device, electronic equipment and storage medium - Google Patents
Vehicle overload alarm method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a vehicle overload alarm method, a vehicle overload alarm device, electronic equipment and a storage medium, wherein the method comprises the steps of obtaining target image data containing a vehicle to be detected; acquiring identification information of a vehicle to be detected in target image data; determining a passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information; judging whether the loading and unloading behaviors exist or not according to the image data; if the behavior of getting on or off the bus exists, determining the number of people getting on or off the bus; updating the current passenger number according to the number of passengers getting on or off the bus; judging the updated current passenger carrying number and the size of the passenger carrying threshold; and if the updated current passenger carrying number is larger than the passenger carrying threshold value, triggering overload alarm aiming at the vehicle to be detected. And determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected through the target image data, and triggering overload alarm aiming at the vehicle to be detected when the current passenger carrying number is greater than the passenger carrying threshold value. The vehicle overload alarming method can realize the alarming of the overload of the vehicle.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a vehicle overload alarm method, a vehicle overload alarm device, electronic equipment and a storage medium.
Background
The vehicle overloading may cause the vehicle weight to increase, which may increase the braking distance and the danger. Overloading the vehicle can also increase the load on the tires, causing them to deform or cause a flat tire, thereby causing a hazard. In addition, overload affects the steering performance of the vehicle, and accidents are easily caused by out-of-control steering. The driver drives the vehicle that transfinites and overloads, psychological burden and thought pressure that often can increase appear operating error easily, cause the traffic accident. And once a traffic accident occurs, additional personal injury is brought.
In urban traffic, overload behaviors of vehicles such as taxis and private cars are common, and therefore it is desirable to alarm for vehicle overload.
Disclosure of Invention
The embodiment of the invention aims to provide a vehicle overload alarm method, a vehicle overload alarm device, electronic equipment and a storage medium, so as to realize the alarm of vehicle overload. The specific technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a vehicle overload warning method, where the method includes:
acquiring target image data containing a vehicle to be detected;
acquiring identification information of the vehicle to be detected in the target image data;
determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information;
judging whether a loading and unloading behavior exists or not according to the target image data;
if the behavior of getting on or off the bus exists, determining the number of people getting on or off the bus;
updating the current passenger carrying number according to the number of people getting on or off the bus;
judging the updated current passenger carrying number and the size of the passenger carrying threshold;
and if the updated current passenger carrying number is larger than the passenger carrying threshold value, triggering overload alarm aiming at the vehicle to be detected.
Optionally, in the vehicle overload warning method according to the embodiment of the present invention, the identification information includes license plate information and vehicle type information;
determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information, wherein the determining comprises the following steps:
acquiring the current passenger carrying number of the vehicle to be detected according to the license plate information;
and acquiring the passenger carrying threshold value of the vehicle to be detected according to the vehicle type information.
Optionally, in the vehicle overload warning method according to the embodiment of the present invention, the identification information includes vehicle type information and license plate information, and the method further includes:
judging whether the vehicle type of the vehicle to be detected is a determinable vehicle type or not according to the vehicle type information;
determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information, wherein the determining comprises the following steps:
if the vehicle type of the vehicle to be detected is a determinable vehicle type, determining a passenger carrying threshold corresponding to the vehicle type information according to the vehicle type information to obtain the passenger carrying threshold of the vehicle to be detected; and acquiring the current passenger carrying number of the vehicle to be detected according to the license plate information.
Optionally, the determining whether the loading/unloading behavior exists according to the image data includes:
detecting and analyzing the image data through a pre-trained deep learning model, and judging whether a loading or unloading behavior exists;
the process of training the deep learning model in advance comprises the following steps:
acquiring a plurality of image data containing getting-on and getting-off behaviors and a plurality of image data not containing getting-on and getting-off behaviors;
and marking the image data containing the getting-on and getting-off behaviors as positive samples, marking the image data not containing the getting-on and getting-off behaviors as negative samples, and training the deep learning model to obtain a pre-trained deep learning model.
Optionally, if there is an act of getting on or off the vehicle, determining the number of people getting on or off the vehicle, including:
if the getting-on/off behavior exists, acquiring a video frame set containing the getting-on/off behavior;
identifying the vehicle position of the vehicle to be detected and the personnel positions of the personnel getting on or off the vehicle in the video frame set;
analyzing the change situation of the personnel position of each person getting on or off the bus according to the time sequence to obtain the moving direction of each person getting on or off the bus;
for each person getting on or off the bus, if the moving direction of the person getting on or off the bus deviates from the position of the bus, judging that the person getting on or off the bus gets off the bus;
and for each person getting on or off the bus, if the moving direction of the person getting on or off the bus points to the position of the bus, judging that the person getting on or off the bus gets on the bus.
In a second aspect, an embodiment of the present invention provides a vehicle overload warning device, where the device includes:
the image data acquisition module is used for acquiring target image data containing a vehicle to be detected;
the identification information acquisition module is used for acquiring identification information of the vehicle to be detected in the target image data;
the first calculation module is used for determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information;
the first judgment module is used for the image data acquisition module and is used for judging whether the loading and unloading behaviors exist or not according to the target image data;
the second calculation module is used for determining the number of people getting on or off the bus if the behavior of getting on or off the bus exists;
the number updating module is used for updating the current passenger carrying number according to the number of people getting on or off the bus;
the second judgment module is used for judging the updated current passenger carrying number and the size of the passenger carrying threshold;
and the overload alarm module is used for triggering overload alarm aiming at the vehicle to be detected if the updated current passenger carrying number is larger than the passenger carrying threshold value.
Optionally, in the vehicle overload warning apparatus according to the embodiment of the present invention, the identification information includes license plate information and vehicle type information;
the first computing module, comprising:
the number obtaining submodule is used for obtaining the current passenger carrying number of the vehicle to be detected according to the license plate information;
and the threshold value acquisition submodule is used for acquiring the passenger carrying threshold value of the vehicle to be detected according to the vehicle type information.
Optionally, in the vehicle overload warning apparatus according to the embodiment of the present invention, the identification information includes vehicle type information and license plate information, and the apparatus further includes:
the third judging module is used for judging whether the vehicle type of the vehicle to be detected is a determinable vehicle type or not according to the vehicle type information;
the first calculation module is specifically configured to:
if the vehicle type of the vehicle to be detected is a determinable vehicle type, determining a passenger carrying threshold corresponding to the vehicle type information according to the vehicle type information to obtain the passenger carrying threshold of the vehicle to be detected; and acquiring the current passenger carrying number of the vehicle to be detected according to the license plate information.
Optionally, the first determining module is specifically configured to:
detecting and analyzing the image data through a pre-trained deep learning model, and judging whether a loading or unloading behavior exists;
the process of training the deep learning model in advance comprises the following steps:
acquiring a plurality of image data containing getting-on and getting-off behaviors and a plurality of image data not containing getting-on and getting-off behaviors;
and marking the image data containing the getting-on and getting-off behaviors as positive samples, marking the image data not containing the getting-on and getting-off behaviors as negative samples, and training the deep learning model to obtain a pre-trained deep learning model.
Optionally, the second computing module includes:
the video frame acquisition submodule is used for acquiring a video frame set containing the getting-on and getting-off behaviors if the getting-on and getting-off behaviors exist;
the position determining submodule is used for identifying the vehicle position of the vehicle to be detected and the personnel position of each person getting on or off the vehicle in the video frame set;
the direction determining submodule is used for analyzing the change condition of the personnel position of each person getting on or off the bus according to a time sequence order to obtain the moving direction of each person getting on or off the bus;
the getting-off judgment sub-module is used for judging that the person getting on or off the vehicle gets off if the moving direction of the person getting on or off the vehicle deviates from the position of the vehicle;
and the getting-on and getting-off judgment submodule is used for judging that the person getting on or off the vehicle gets on the vehicle if the moving direction of the person getting on or off the vehicle points to the position of the vehicle.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to implement the vehicle overload warning method according to any one of the first aspect described above when executing the program stored in the memory.
In a fourth aspect, the present invention provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the vehicle overload warning method according to any one of the first aspect.
The vehicle overload alarm method, the vehicle overload alarm device, the electronic equipment and the storage medium provided by the embodiment of the invention are used for acquiring target image data containing a vehicle to be detected; acquiring identification information of a vehicle to be detected in target image data; determining a passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information; judging whether the loading and unloading behaviors exist or not according to the image data; if the behavior of getting on or off the bus exists, determining the number of people getting on or off the bus; updating the current passenger number according to the number of passengers getting on or off the bus; judging the updated current passenger carrying number and the size of the passenger carrying threshold; and if the updated current passenger carrying number is larger than the passenger carrying threshold value, triggering overload alarm aiming at the vehicle to be detected. And determining the passenger carrying threshold and the current passenger carrying number of the vehicle to be detected through the target image data, and triggering overload alarm aiming at the vehicle to be detected when the current passenger carrying number is greater than the passenger carrying threshold, so that the overload alarm of the vehicle can be realized. Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic view of a vehicle overload warning device according to an embodiment of the present invention;
FIG. 2a is a first schematic diagram of a method for determining a driving direction of a person according to an embodiment of the present invention;
FIG. 2b is a second schematic diagram of the method for determining the driving direction of a person according to the embodiment of the present invention;
FIG. 2c is a third schematic diagram of a method for determining a driving direction of a person according to an embodiment of the present invention;
FIG. 3 is a flow chart of a vehicle overload warning method according to an embodiment of the present invention;
FIG. 4 is another flow chart of a vehicle overload warning method according to an embodiment of the invention;
FIG. 5 is another schematic view of a vehicle overload warning device in accordance with an embodiment of the present invention;
fig. 6 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Interpretation of terms:
intelligent transportation: the comprehensive traffic transportation management system is established by effectively integrating and applying information technology, data communication transmission technology, electronic sensing technology, control technology, computer technology and the like to the whole ground traffic management system, plays a role in a large range in all directions, and has the characteristics of real time, accuracy and high efficiency. The method is characterized in that information collection, processing, publishing, exchange, analysis and utilization are used as a main line, and diversified services are provided for traffic participants.
Target detection: also called target extraction, is to extract the object of interest from the complex background image.
Image classification: an image processing method for distinguishing objects of different classes based on different characteristics of the objects reflected in image information. It uses computer to make quantitative analysis of image, and classifies each picture element or region in the image into one of several categories to replace human visual interpretation.
Target identification: for separating a given target from other targets.
CNN (Convolutional Neural Networks) is a machine learning model under deep supervised learning, and is the first learning algorithm capable of successfully training a multi-layer network structure. The method utilizes the spatial relationship to reduce the number of parameters to be learned so as to improve the training performance of the general forward BP (error Back propagation) algorithm.
Fast RCNN (fast Regions relational Neural Network): the method is the best method for detecting targets based on a deep learning RCNN (regions With relational Network targets) series, and is a framework for realizing end-to-end target detection in a CNN (relational Network) Network. Where R corresponds to Region.
Yolo (young Only Look once): YOLO is a very successful real-time target detection algorithm, has become an important part of many classifiers, segmentation, human body posture and behavior classification at present, and is a target detection framework for realizing end-to-end of a CNN network.
In urban traffic, overload behaviors of taxis and private vehicles are very common. The overload behavior has obvious traffic safety hidden trouble, and once a traffic accident occurs, additional personnel and economic loss are brought.
The vehicle overload alarm method, the vehicle overload alarm device, the electronic equipment and the storage medium can be applied to urban road monitoring video analysis, and whether the vehicle has the behavior of overmaning or not is judged by judging the vehicle type and counting the number of passengers getting off the vehicle in a short time. And the behavior is recorded by recording the license plate number, so that the behavior is used for standardizing the vehicle behavior by the traffic department.
The main monitoring object of the embodiment of the invention can be a small vehicle such as a taxi, a private car and the like in a city. Referring to fig. 1, a vehicle overload warning apparatus according to an embodiment of the present invention includes: the license plate recognition module 101, the vehicle type recognition module 102, the number of passengers getting on and off counting module 103 and the discrimination alarm module 104. The license plate recognition module 101 is used for recognizing a license plate of a vehicle, and the vehicle type recognition module 102 is used for recognizing a vehicle type of the vehicle, is mature in the prior art, and can directly reuse a related technology. The judgment alarm module 104 is used for giving an alarm when the number of people carried by the vehicle exceeds the number threshold of people carried by the vehicle.
The number counting module 103 is used for counting the number of passengers getting on or off the vehicle, and counting the behaviors of getting on or off the vehicle by tracking pedestrians on two sides of the vehicle. The pedestrian detection method comprises the steps that for the getting-on and getting-off behaviors of all pedestrians, the pedestrians are used as targets for detection through a target detection algorithm; generating a pedestrian movement track; and judging the behavior of passengers getting on and off according to the track direction. And (4) counting the total number of people getting on/off the vehicle by integrating the getting on/off behavior of each pedestrian so as to obtain the number of people borne by the vehicle.
For example, the video data is detected by an object detection algorithm, and the positions where pedestrians are detected are shown as rectangular boxes in fig. 2 a. The video data is continuously detected, the new position of the pedestrian is detected as shown by a rectangular frame in fig. 2b, the overlapping degree is larger than 0.5 when compared with the position of the pedestrian in the previous frame (shown by a dashed line frame in fig. 2 b), and the target (pedestrian) in the two frames of video frames is associated. The video data is continuously analyzed, and the result of detecting the pedestrian of the multiple frames of the associated video frames is shown in fig. 2c, wherein only four frames are taken as an example, and the number of the associated frames can be set by self in the actual detection process. And calculating the pedestrian track direction as leaving the vehicle according to the result of multiple frames, and judging the behavior of getting off the vehicle.
Specifically, the flow of the vehicle overload warning method according to the embodiment of the present invention can be as shown in fig. 3, and the embodiment of the present invention can realize automatic identification of overload behaviors, locate an overloaded vehicle by analyzing an urban road monitoring video, and record an illegal behavior thereof, which is helpful for enhancing the monitoring of traffic management departments on such behaviors.
The monitoring equipment for collecting images in the embodiment of the invention can be off-board, can directly utilize public monitoring equipment of urban traffic, and has the following advantages: the supervision department does not need to install equipment on each vehicle to be supervised, but only needs to add the functions in the existing urban intelligent traffic monitoring system, so that the capture of overload behaviors can be completed, and the cost is greatly reduced. And can reduce the situation of human interference of a supervised person, such as: the vehicle-mounted camera is obstructed by destroying or coating, shading, adjusting the direction of the camera and the like. And the overload of the vehicles of different vehicle types can be judged by combining the vehicle types. For example, the upper limit of the number of passengers getting off a taxi is 4, and the upper limit of the number of passengers getting off a common car is 5.
In order to alarm an overloaded vehicle, an embodiment of the present invention provides a vehicle overload alarm method, and referring to fig. 4, the method includes:
s401, target image data containing a vehicle to be detected is obtained.
The vehicle overload warning method in the embodiment of the invention can be realized by a warning system, and the warning system is any system capable of realizing the vehicle overload warning method in the embodiment of the invention. For example:
the alarm system may be an apparatus comprising: a processor, a memory, a communication interface, and a bus; the processor, the memory and the communication interface are connected through a bus and complete mutual communication; the memory stores executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the vehicle overload warning method of the embodiment of the invention.
The warning system can also be an application program used for executing the vehicle overload warning method in the embodiment of the invention when the application program runs.
The warning system may also be a storage medium for storing executable code for performing the vehicle overload warning method of embodiments of the present invention.
The alarm system obtains image data including a vehicle to be detected, namely target image data, through the image acquisition equipment. The image acquisition device can be a camera and other devices, and optionally, the image acquisition device is a camera in an urban traffic monitoring system.
S402, acquiring identification information of the vehicle to be detected in the target image data;
and analyzing the target image data through a target detection algorithm to obtain the identification information of the vehicle to be detected, wherein the identification information is used for uniquely identifying the vehicle to be detected. For example, the target image data is analyzed through the RCNN, fast RCNN, YOLO, or the like, so as to obtain the license plate information of the vehicle to be detected.
And S403, determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information.
The identification information is unique identification information of the vehicle, the alarm system keeps recording the current passenger carrying number of each vehicle, each vehicle corresponds to a corresponding passenger carrying threshold value, and the passenger carrying threshold value of the vehicle is set according to related traffic regulations, for example, the passenger carrying threshold value corresponding to the vehicle with the type of a taxi is 4, the passenger carrying threshold value corresponding to the vehicle with the type of a private car is 5, and the like.
The alarm system keeps recording the current passenger carrying number of each vehicle, when the vehicle at a parking spot is started, video data before and after the vehicle is started are obtained and used as target video data, the initial current passenger carrying number of the vehicle is defaulted to be 0, and the current passenger carrying number of the vehicle is updated through the vehicle overload alarm method provided by the embodiment of the invention. And then, keeping recording the current passenger carrying number of the vehicle by the vehicle overload alarming method of the embodiment of the invention.
And S404, judging whether the getting-on/off behavior exists according to the target image data.
The alarm system analyzes the target image data through a preset algorithm, such as a feature comparison algorithm or a neural network algorithm, and judges whether the loading and unloading behaviors exist or not.
Optionally, the determining whether the loading/unloading behavior exists according to the image data includes:
detecting and analyzing the image data through a pre-trained deep learning model, and judging whether a loading or unloading behavior exists;
the process of training the deep learning model in advance comprises the following steps:
acquiring a plurality of image data containing getting-on/off behaviors and a plurality of image data not containing getting-on/off behaviors;
and step two, marking the image data containing the getting-on and getting-off behaviors as positive samples, marking the image data not containing the getting-on and getting-off behaviors as negative samples, and training the deep learning model to obtain a pre-trained deep learning model.
The deep learning model may be fast RCNN, YOLO, RCNN (or DMP (Deformable part model), or the like.
And S405, if the behavior of getting on or off the bus exists, determining the number of people getting on or off the bus.
When the behavior of getting on or off the bus exists, the alarm system determines the number of people getting on or off the bus. For example, the alarm system may analyze a video frame segment containing a getting-on/off behavior in the target video data through a pre-trained convolutional neural network to determine the number of people getting-on/off the vehicle.
The step of pre-training the convolutional neural network comprises:
step one, acquiring a plurality of first image data containing getting-on/off behaviors and a plurality of second image data not containing getting-on/off behaviors
Calibrating the number of people who get on the bus and the number of people who get on the bus in each first image data, and calibrating the number of people who get off the bus and the number of people who get off the bus in each image data to obtain calibrated first image data;
and step three, taking the calibrated first image data as a positive sample, taking the second image data as a negative sample, and training the convolutional neural network to obtain a pre-trained convolutional neural network.
And S406, updating the current passenger carrying number according to the number of the passengers getting on or off the bus.
And when the alarm system detects that one person gets off, the current passenger carrying number of the vehicle to be detected is reduced by 1, and when the alarm system detects that one person gets on, the current passenger carrying number of the vehicle to be detected is increased by 1, so that the current passenger carrying number is updated.
And S407, judging the updated current passenger carrying number and the size of the passenger carrying threshold.
And S408, if the updated current passenger carrying number is larger than the passenger carrying threshold value, triggering overload alarm aiming at the vehicle to be detected.
The alarm system triggers overload alarm aiming at the vehicle to be detected, for example, the alarm system alarms the identification information and the current passenger number of the vehicle to be detected to a subscription terminal, and can also alarm the position of the vehicle to be detected, wherein the position of the vehicle to be detected can be acquired through a camera for acquiring target video data.
In the embodiment of the invention, automatic alarm of vehicle overload can be realized, the monitoring equipment for acquiring target image data can be off-board, the public monitoring equipment of urban traffic can be directly used for acquiring the target image data, and the supervision department does not need to install equipment on each vehicle to be monitored, but only needs to add the above functions in the existing urban intelligent traffic monitoring system, so that capture of overload behavior can be completed, the cost is greatly reduced, and the condition of artificial interference of a supervised person can be reduced.
Optionally, the identification information includes license plate information and vehicle type information;
correspondingly, the determining the passenger threshold and the current passenger number of the vehicle to be detected according to the identification information includes:
step one, acquiring the current passenger carrying number of the vehicle to be detected according to the license plate information.
The warning system stores the number of passengers carried by each vehicle in a designated memory. When the vehicle number detecting device is used, the alarm system reads the current passenger carrying number of the vehicle to be detected in the memory according to the license plate information.
And step two, acquiring the passenger carrying threshold value of the vehicle to be detected according to the vehicle type information.
According to the type information of the vehicle to be detected, determining a passenger carrying threshold corresponding to the type of the vehicle to be detected, namely determining the passenger carrying threshold of the vehicle to be detected, wherein the passenger carrying threshold of the vehicle to be detected is 4 people, and the passenger carrying threshold of a common car is 5 people.
In the embodiment of the invention, the vehicle to be detected can be uniquely determined through the license plate information, and the passenger carrying threshold value of the vehicle to be detected can be determined through the vehicle type information, so that the method and the device are convenient and quick.
Optionally, the identification information includes vehicle type information and license plate information, and the method further includes:
judging whether the vehicle type of the vehicle to be detected is a determinable vehicle type or not according to the vehicle type information;
correspondingly, the determining the passenger threshold and the current passenger number of the vehicle to be detected according to the identification information includes:
if the vehicle type of the vehicle to be detected is a determinable vehicle type, determining a passenger carrying threshold corresponding to the vehicle type information according to the vehicle type information to obtain the passenger carrying threshold of the vehicle to be detected; and acquiring the current passenger carrying number of the vehicle to be detected according to the license plate information.
And judging that the vehicle type of the vehicle to be detected is not a determinable vehicle type by the alarm system, and finishing the overload judgment.
In the embodiment of the invention, the alarm system can set the determinable vehicle type, and only performs the getting-on/off behavior and the subsequent judgment aiming at the specified vehicle type, so that the overload judgment of the vehicles except the determinable vehicle type can be avoided, and the calculation resources are saved.
Optionally, the determining the number of people getting on or off the vehicle if the behavior of getting on or off the vehicle exists includes:
step one, if the getting-on/off behavior exists, acquiring a video frame set containing the getting-on/off behavior.
When the alarm system judges that the getting-on/off behavior exists in the target video data, the alarm system extracts a video frame set containing the characteristics of the getting-on/off behavior.
And step two, identifying the vehicle position of the vehicle to be detected and the personnel positions of the passengers getting on or off the vehicle in the video frame set.
And the alarm system determines the vehicle position of the vehicle to be detected and the personnel positions of the passengers getting on or off the vehicle in each video frame of the video frame set through a target recognition algorithm.
And step three, analyzing the change situation of the personnel positions of the passengers getting on and off the bus according to a time sequence to obtain the moving direction of the passengers getting on and off the bus.
And analyzing the change condition of the positions of the persons getting on and off the bus according to the time sequence of the video frames to obtain the moving direction of the persons getting on and off the bus. For example, for each person getting on or off the bus, the change situation of the position of the person getting on or off the bus is determined by a target tracking algorithm, so that the moving direction of the person getting on or off the bus is obtained; or calculating the change condition of the position central point of the person getting on or off the bus to obtain the moving direction of the person getting on or off the bus.
Selecting a central point of the personnel position of the passengers for getting on and off according to the time sequence of the video frames, wherein the coordinate of the central point is expressed as (X)i，Yi). Wherein, XiX-coordinate, Y, representing the video frame of the ith frameiRepresenting the Y coordinate of the video frame of the ith frame by
Wherein t is a smooth interval and can be 1-5.
If the number of D1-D5 is more than 0, and the number of D1-D5 is less than 0, the person is judged to move along the positive direction of the X axis, otherwise, the person is judged to move along the negative direction of the X axis.
Similarly, the direction of movement of the person along the Y-axis can be found.
And step four, for each person getting on or off the vehicle, if the moving direction of the person getting on or off the vehicle deviates from the position of the vehicle, judging that the person getting on or off the vehicle gets off the vehicle.
And step five, for each person getting on or off the vehicle, if the moving direction of the person getting on or off the vehicle points to the position of the vehicle, judging that the person getting on or off the vehicle gets on the vehicle.
In the embodiment of the invention, a specific method for counting the number of people getting on or off the bus is provided, and the calculation is convenient.
An embodiment of the present invention further provides a vehicle overload warning device, referring to fig. 5, where the device includes:
an image data obtaining module 501, configured to obtain target image data including a vehicle to be detected;
an identification information obtaining module 502, configured to obtain identification information of the vehicle to be detected from the target image data;
a first calculating module 503, configured to determine a passenger threshold and a current passenger number of the vehicle to be detected according to the identification information;
a first judging module 504, configured to be an image data obtaining module, configured to judge whether a loading/unloading behavior exists according to the target image data;
a second calculating module 505, configured to determine the number of people getting on or off the vehicle if there is a behavior of getting on or off the vehicle;
a number updating module 506, configured to update the current number of passengers according to the number of people getting on or off the train;
a second judgment module 507, configured to judge the updated current passenger carrying number and the size of the passenger carrying threshold;
and the overload alarm module 508 is configured to trigger an overload alarm for the vehicle to be detected if the updated current number of passengers is greater than the passenger threshold.
In the embodiment of the invention, automatic alarm of vehicle overload can be realized, the monitoring equipment for acquiring target image data can be off-board, the public monitoring equipment of urban traffic can be directly used for acquiring the target image data, and the supervision department does not need to install equipment on each vehicle to be monitored, but only needs to add the above functions in the existing urban intelligent traffic monitoring system, so that capture of overload behavior can be completed, the cost is greatly reduced, and the condition of artificial interference of a supervised person can be reduced.
Optionally, in the vehicle overload warning apparatus according to the embodiment of the present invention, the identification information includes license plate information and vehicle type information;
the first calculating module 503 includes:
the number obtaining submodule is used for obtaining the current passenger carrying number of the vehicle to be detected according to the license plate information;
and the threshold value obtaining submodule is used for obtaining the passenger carrying threshold value of the vehicle to be detected according to the vehicle type information.
Optionally, in the vehicle overload warning apparatus according to the embodiment of the present invention, the identification information includes vehicle type information and license plate information, and the apparatus further includes:
the third judging module is used for judging whether the vehicle type of the vehicle to be detected is a determinable vehicle type or not according to the vehicle type information;
the first calculating module 503 is specifically configured to:
if the vehicle type of the vehicle to be detected is a determinable vehicle type, determining a passenger carrying threshold corresponding to the vehicle type information according to the vehicle type information to obtain the passenger carrying threshold of the vehicle to be detected; and acquiring the current passenger carrying number of the vehicle to be detected according to the license plate information.
Optionally, the first determining module 504 is specifically configured to:
detecting and analyzing the image data through a pre-trained deep learning model, and judging whether a loading or unloading behavior exists;
the process of training the deep learning model in advance comprises the following steps:
acquiring a plurality of image data containing getting-on and getting-off behaviors and a plurality of image data not containing getting-on and getting-off behaviors;
and marking the image data containing the getting-on and getting-off behaviors as positive samples, marking the image data not containing the getting-on and getting-off behaviors as negative samples, and training the deep learning model to obtain a pre-trained deep learning model.
Optionally, the second calculating module 505 includes:
the video frame acquisition submodule is used for acquiring a video frame set containing the getting-on and getting-off behaviors if the getting-on and getting-off behaviors exist;
the position determining submodule is used for identifying the vehicle position of the vehicle to be detected and the personnel position of each person getting on or off the vehicle in the video frame set;
the direction determining submodule is used for analyzing the change situation of the personnel positions of the people getting on or off the bus according to a time sequence order to obtain the moving direction of the people getting on or off the bus;
a get-off judgment sub-module, configured to judge, for each of the persons getting on and off, that the person getting on and off gets off if the moving direction of the person getting on and off deviates from the vehicle position;
and the getting-on and getting-off judgment submodule is used for judging that the person gets on or off the vehicle if the moving direction of the person getting on or off the vehicle points to the position of the vehicle.
An embodiment of the present invention provides an electronic device, which is shown in fig. 6 and includes a processor 601 and a memory 602;
the memory 602 is used for storing computer programs;
the processor 601 is configured to implement the following steps when executing the program stored in the memory 602:
acquiring target image data containing a vehicle to be detected;
acquiring identification information of the vehicle to be detected in the target image data;
determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information;
judging whether getting on or off the bus exists according to the target image data;
if the behavior of getting on or off the bus exists, determining the number of people getting on or off the bus;
updating the current passenger number according to the number of the passengers getting on or off the bus;
judging the updated current passenger carrying number and the size of the passenger carrying threshold;
and if the updated current passenger carrying number is larger than the passenger carrying threshold value, triggering overload alarm aiming at the vehicle to be detected.
In the embodiment of the invention, automatic alarm of vehicle overload can be realized, the monitoring equipment for acquiring target image data can be off-board, the public monitoring equipment of urban traffic can be directly used for acquiring the target image data, and the supervision department does not need to install equipment on each vehicle to be monitored, but only needs to add the above functions in the existing urban intelligent traffic monitoring system, so that capture of overload behavior can be completed, the cost is greatly reduced, and the condition of artificial interference of a supervised person can be reduced.
Optionally, the processor 601 is configured to implement any one of the vehicle overload warning methods when executing the program stored in the memory 602.
Optionally, the electronic device according to the embodiment of the present invention further includes a communication interface and a communication bus, where the processor 601, the communication interface, and the memory 602 complete mutual communication through the communication bus.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the following steps:
acquiring target image data containing a vehicle to be detected;
acquiring identification information of the vehicle to be detected in the target image data;
determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information;
judging whether getting on or off the bus exists according to the target image data;
if the behavior of getting on or off the bus exists, determining the number of people getting on or off the bus;
updating the current passenger number according to the number of the passengers getting on or off the bus;
judging the updated current passenger carrying number and the size of the passenger carrying threshold;
and if the updated current passenger carrying number is larger than the passenger carrying threshold value, triggering overload alarm aiming at the vehicle to be detected.
In the embodiment of the invention, automatic alarm of vehicle overload can be realized, the monitoring equipment for acquiring target image data can be off-board, the public monitoring equipment of urban traffic can be directly used for acquiring the target image data, and the supervision department does not need to install equipment on each vehicle to be monitored, but only needs to add the above functions in the existing urban intelligent traffic monitoring system, so that capture of overload behavior can be completed, the cost is greatly reduced, and the condition of artificial interference of a supervised person can be reduced.
Optionally, when executed by the processor, the computer program may further implement any one of the above vehicle overload warning methods.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiments of the apparatus, the electronic device, and the storage medium, since they are substantially similar to the method embodiments, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiments.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (12)
1. A vehicle overload warning method, characterized in that the method comprises:
acquiring target image data containing a vehicle to be detected;
acquiring identification information of the vehicle to be detected in the target image data;
determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information;
judging whether a loading and unloading behavior exists or not according to the target image data;
if the behavior of getting on or off the bus exists, determining the number of people getting on or off the bus;
updating the current passenger carrying number according to the number of people getting on or off the bus;
judging the updated current passenger carrying number and the size of the passenger carrying threshold;
and if the updated current passenger carrying number is larger than the passenger carrying threshold value, triggering overload alarm aiming at the vehicle to be detected.
2. The method of claim 1, wherein the identification information includes license plate information and vehicle type information;
determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information, wherein the determining comprises the following steps:
acquiring the current passenger carrying number of the vehicle to be detected according to the license plate information;
and acquiring the passenger carrying threshold value of the vehicle to be detected according to the vehicle type information.
3. The method of claim 1, wherein the identification information includes vehicle type information and license plate information, the method further comprising:
judging whether the vehicle type of the vehicle to be detected is a determinable vehicle type or not according to the vehicle type information;
determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information, wherein the determining comprises the following steps:
if the vehicle type of the vehicle to be detected is a determinable vehicle type, determining a passenger carrying threshold corresponding to the vehicle type information according to the vehicle type information to obtain the passenger carrying threshold of the vehicle to be detected; and acquiring the current passenger carrying number of the vehicle to be detected according to the license plate information.
4. The method of claim 1, wherein determining whether a loading or unloading behavior exists based on the image data comprises:
detecting and analyzing the image data through a pre-trained deep learning model, and judging whether a loading or unloading behavior exists;
the process of training the deep learning model in advance comprises the following steps:
acquiring a plurality of image data containing getting-on and getting-off behaviors and a plurality of image data not containing getting-on and getting-off behaviors;
and marking the image data containing the getting-on and getting-off behaviors as positive samples, marking the image data not containing the getting-on and getting-off behaviors as negative samples, and training the deep learning model to obtain a pre-trained deep learning model.
5. The method of claim 1, wherein determining the number of people getting on or off the vehicle if there is an act of getting on or off the vehicle comprises:
if the getting-on/off behavior exists, acquiring a video frame set containing the getting-on/off behavior;
identifying the vehicle position of the vehicle to be detected and the personnel positions of the personnel getting on or off the vehicle in the video frame set;
analyzing the change situation of the personnel position of each person getting on or off the bus according to the time sequence to obtain the moving direction of each person getting on or off the bus;
for each person getting on or off the bus, if the moving direction of the person getting on or off the bus deviates from the position of the bus, judging that the person getting on or off the bus gets off the bus;
and for each person getting on or off the bus, if the moving direction of the person getting on or off the bus points to the position of the bus, judging that the person getting on or off the bus gets on the bus.
6. A vehicle overload warning device, the device comprising:
the image data acquisition module is used for acquiring target image data containing a vehicle to be detected;
the identification information acquisition module is used for acquiring identification information of the vehicle to be detected in the target image data;
the first calculation module is used for determining the passenger carrying threshold value and the current passenger carrying number of the vehicle to be detected according to the identification information;
the first judgment module is used for the image data acquisition module and is used for judging whether the loading and unloading behaviors exist or not according to the target image data;
the second calculation module is used for determining the number of people getting on or off the bus if the behavior of getting on or off the bus exists;
the number updating module is used for updating the current passenger carrying number according to the number of people getting on or off the bus;
the second judgment module is used for judging the updated current passenger carrying number and the size of the passenger carrying threshold;
and the overload alarm module is used for triggering overload alarm aiming at the vehicle to be detected if the updated current passenger carrying number is larger than the passenger carrying threshold value.
7. The apparatus of claim 6, wherein the identification information includes license plate information and vehicle type information;
the first computing module, comprising:
the number obtaining submodule is used for obtaining the current passenger carrying number of the vehicle to be detected according to the license plate information;
and the threshold value acquisition submodule is used for acquiring the passenger carrying threshold value of the vehicle to be detected according to the vehicle type information.
8. The apparatus of claim 6, wherein the identification information includes vehicle type information and license plate information, the apparatus further comprising:
the third judging module is used for judging whether the vehicle type of the vehicle to be detected is a determinable vehicle type or not according to the vehicle type information;
the first calculation module is specifically configured to:
if the vehicle type of the vehicle to be detected is a determinable vehicle type, determining a passenger carrying threshold corresponding to the vehicle type information according to the vehicle type information to obtain the passenger carrying threshold of the vehicle to be detected; and acquiring the current passenger carrying number of the vehicle to be detected according to the license plate information.
9. The apparatus of claim 6, wherein the first determining module is specifically configured to:
detecting and analyzing the image data through a pre-trained deep learning model, and judging whether a loading or unloading behavior exists;
the process of training the deep learning model in advance comprises the following steps:
acquiring a plurality of image data containing getting-on and getting-off behaviors and a plurality of image data not containing getting-on and getting-off behaviors;
and marking the image data containing the getting-on and getting-off behaviors as positive samples, marking the image data not containing the getting-on and getting-off behaviors as negative samples, and training the deep learning model to obtain a pre-trained deep learning model.
10. The apparatus of claim 6, wherein the second computing module comprises:
the video frame acquisition submodule is used for acquiring a video frame set containing the getting-on and getting-off behaviors if the getting-on and getting-off behaviors exist;
the position determining submodule is used for identifying the vehicle position of the vehicle to be detected and the personnel position of each person getting on or off the vehicle in the video frame set;
the direction determining submodule is used for analyzing the change condition of the personnel position of each person getting on or off the bus according to a time sequence order to obtain the moving direction of each person getting on or off the bus;
the getting-off judgment sub-module is used for judging that the person getting on or off the vehicle gets off if the moving direction of the person getting on or off the vehicle deviates from the position of the vehicle;
and the getting-on and getting-off judgment submodule is used for judging that the person getting on or off the vehicle gets on the vehicle if the moving direction of the person getting on or off the vehicle points to the position of the vehicle.
11. An electronic device comprising a processor and a memory;
the memory is used for storing a computer program;
the processor, when executing the program stored in the memory, implementing the method steps of any of claims 1-5.
12. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of the claims 1-5.
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| CN201811061118.XA CN110895662A (en) | 2018-09-12 | 2018-09-12 | Vehicle overload alarm method and device, electronic equipment and storage medium |
| PCT/CN2019/103030 WO2020052436A1 (en) | 2018-09-12 | 2019-08-28 | Vehicle overload alarming method and apparatus, electronic device, and storage medium |
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| CN201811061118.XA CN110895662A (en) | 2018-09-12 | 2018-09-12 | Vehicle overload alarm method and device, electronic equipment and storage medium |
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