CN112270244A - Target violation monitoring method and device, electronic equipment and storage medium - Google Patents
Target violation monitoring method and device, electronic equipment and storage medium Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/07—Target detection
Abstract
The invention relates to the field of monitoring, and discloses a target violation monitoring method, which comprises the following steps: carrying out target object position detection on the target object picture set to obtain a target frame; classifying the target frames, and identifying each target object according to the classification; performing tracking inspection on each target object to obtain a tracking track of each target object, and combining the tracking tracks to obtain a tracking track set; carrying out violation analysis on each tracking track in the tracking track set to obtain a corresponding violation target; and storing the violation target object and the corresponding tracking track into a violation database. The invention also provides a device for monitoring violation of target objects, electronic equipment and a computer-readable storage medium. The invention also relates to a block chain technology, and the illegal target object and the tracking track can be saved in the block chain node. The invention can improve the identification rate and efficiency of violation monitoring and reduce the computing resources consumed by monitoring.
Description
Technical Field
The present invention relates to the field of monitoring, and in particular, to a method and an apparatus for monitoring violation of a target, an electronic device, and a computer-readable storage medium.
Background
The law and regulation clearly inform people in the form of legal provisions of what can be done, what can not be done, what behaviors are legal and what behaviors are illegal, and the law and regulation leads the illegal person to be subjected to corresponding sanctions. Violations of legal regulations may adversely affect maintaining social stability. For example, with the rapid development of economy, the number of vehicles is rapidly increased, and phenomena violating traffic regulations are increasingly serious, such as illegal parking, random lane changing without turning on a steering lamp, illegal turning around, reverse running, random passengers getting on and off, and the like. These behaviors violating traffic regulations are likely to threaten the security of people's lives and property. Thus, it is important to deter and combat violations of traffic regulations.
However, due to limited police force, such illegal behaviors often cannot be checked in time, a lot of traffic participants have a lucky psychology, the existing electronic police plays an effective deterrence role, but the electronic police mainly relies on license plates for vehicle identification, and under some conditions, the condition that illegal monitoring fails due to the fact that the license plates cannot be identified may exist, so that monitoring and identification efficiency is low.
Disclosure of Invention
The embodiment of the invention provides a method and a device for monitoring violation of rules of a target object, electronic equipment and a computer-readable storage medium, and mainly aims to improve the recognition rate and efficiency of violation monitoring and reduce the computing resources consumed by monitoring.
In order to achieve the above object, the present invention provides a method for monitoring violation of a target, including:
acquiring a target object picture set, and carrying out target object position detection on the target object picture set to obtain a target frame;
classifying the target frames by a local identification method and/or an overall appearance identification method, and identifying each target object according to a classification result;
performing tracking inspection on each target object to obtain a tracking track of each target object, and combining the tracking tracks to obtain a tracking track set;
carrying out violation analysis on each tracking track in the tracking track set to obtain violation targets and corresponding tracking tracks;
and storing the violation target object and the corresponding tracking track into a violation database.
Optionally, the acquiring the target object picture set includes:
continuously shooting vehicles running in a preset section to obtain a preset number of continuously shot images, and forming the preset number of continuously shot images into the target object picture set.
Optionally, the classifying the target frame by a local recognition method and/or an overall appearance recognition method, and recognizing each target according to a classification result includes:
capturing license plate information in the target frame through a pre-constructed license plate recognition algorithm, and classifying the target frame according to the license plate information;
and when the license plate information cannot be captured, performing feature extraction on the vehicle through a pre-constructed feature extraction model, and classifying the target frame by using the extracted features.
Optionally, the extracting the features of the vehicle through the pre-constructed feature extraction model, and classifying the target frame by using the extracted features includes:
primarily classifying the target frame according to the appearance and color characteristics of the vehicle;
and performing secondary classification on the basis of the primary classification according to the traveling track of the vehicle.
Optionally, the obtaining a target object picture set, and performing target object position detection on the target object picture set to obtain a target frame further includes:
obtaining a vehicle judgment model by utilizing a classification network which is pre-constructed by training a preset number of normal vehicles and special vehicles;
and identifying a special vehicle from the target frame by using the vehicle judgment model, and deleting the target frame corresponding to the special vehicle.
Optionally, the violation analysis is performed on each tracking track in the tracking track set to obtain a violation target and a corresponding tracking track, and the method includes:
identifying traffic element information in the target object picture set, and generating a compliant driving area of the target object by using the traffic element information;
judging the driving compliance of the target object according to whether the tracking track exceeds the range of the compliant driving area;
and when the running of the target object is not in compliance, extracting the target object and a target object picture set corresponding to the target object to obtain the illegal target object and a corresponding tracking track.
Optionally, the identifying the traffic element information in the target object picture set includes:
preprocessing the target object picture set by utilizing a super-resolution reconstruction technology;
segmenting the road information of the preprocessed target object picture set, and extracting a lane line to obtain a lane line identifier;
and classifying the lane line identification to obtain the traffic element information in the target object picture set.
In order to solve the above problem, the present invention further provides a device for monitoring violation of a target, the device including:
the target object detection module is used for acquiring a target object picture set and carrying out target object position detection on the target object picture set to obtain a target frame;
the target object classification module is used for classifying the target frames through a local identification method and/or an overall appearance identification method, and identifying each target object according to a classification result;
the target violation monitoring module is used for tracking and checking each target to obtain a tracking track of each target, combining the tracking tracks to obtain a tracking track set, and carrying out violation analysis on each tracking track in the tracking track set to obtain a violation target and a corresponding tracking track;
and the evidence storage module is used for storing the violation target object and the corresponding tracking track into a violation database.
In order to solve the above problem, the present invention also provides an electronic device, including: at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the method for target violation monitoring as described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium comprising a storage data area and a storage program area, wherein the storage data area stores created data, and the storage program area stores a computer program; wherein the computer program, when executed by a processor, implements the method for monitoring violation of a target object as described above.
According to the embodiment of the invention, the target frame is obtained by obtaining the target object picture set and carrying out target object position detection on the target object picture set, and the preset number of pictures are analyzed to replace the video, so that the consumption of computing and storing resources can be reduced. By means of the local identification and/or overall appearance identification method and tracking and checking of each target object, vehicle information and tracking tracks can be completely controlled, and the problem of monitoring failure caused by traffic jam and mutual shielding is avoided. According to the embodiment of the invention, the violation target and the corresponding tracking track are stored in the violation database, so that the violation evidence can be stored at the first time when the traffic violation is found, and the violation evidence obtaining efficiency is increased. Therefore, the embodiment of the invention can improve the violation monitoring recognition rate and the monitoring efficiency and reduce the computing resources consumed by monitoring.
Drawings
Fig. 1 is a schematic flowchart of a target violation monitoring method according to an embodiment of the present invention;
fig. 2 is a block diagram of a device for monitoring violation of a target object according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a method for monitoring violation of a target object according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a method for monitoring violation of a target object. The execution subject of the target violation monitoring method provided by the embodiment of the present application includes, but is not limited to, at least one of electronic devices that can be configured to execute the method provided by the embodiment of the present application, such as a server and a terminal. In other words, the target violation monitoring method may be executed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to a flow diagram of a target violation monitoring method shown in fig. 1, the target violation monitoring method according to the embodiment of the present invention includes:
and S1, acquiring a target object picture set, and carrying out target object position detection on the target object picture set to obtain a target frame.
The embodiment of the invention can shoot the picture set of the target object through any video or image shooting equipment. Continuously shooting vehicles running in a preset section to obtain a preset number of continuously shot images, and forming the preset number of continuously shot images into the target object picture set.
According to the embodiment of the invention, a monitoring camera fixed at a traffic intersection can be utilized to continuously shoot the vehicles from appearance to disappearance when the vehicles pass through, photos with the same time interval of preset Q number are selected to form the target object picture set, and Q can be 4.
Further, the target object in the embodiment of the present invention is mainly a target object that may violate a traffic regulation, and may be a vehicle or a person. Preferably, in the embodiment of the present invention, the target object is a vehicle traveling in a preset section. The target frame is a kind of identification pattern that highlights the target.
In another embodiment of the present invention, after S1, the method further includes:
obtaining a vehicle judgment model by utilizing a classification network which is pre-constructed by training a preset number of normal vehicles and special vehicles;
and identifying a special vehicle from the target frame by using the vehicle judgment model, and deleting the target frame corresponding to the special vehicle.
The classification network of the embodiment of the invention can be a MobileNet V3 classification network.
The MobileNetV3 classification network can be divided into a start part, a middle part, and a last part. The initial part comprises 1 convolutional layer, and features are extracted through convolution of 3x 3; the middle part is a network structure of a plurality of blocks (MobileBlock) containing convolutional layers; the final part outputs the category by two 1x1 convolutional layers instead of full connection.
In detail, the embodiment of the invention carries out classification training on the MobileNet 3 classification network by collecting and labeling the training data of 18 types of vehicles including normal vehicles and special vehicles, and judges whether the vehicles belong to the special vehicles. Preferably, the special vehicle may include, but is not limited to, police cars, ambulances, city sweepers, and the like.
And S2, classifying the target frames by a local identification method and/or an overall appearance identification method, and identifying each target object according to the classification result.
In the embodiment of the invention, the local recognition can be license plate recognition and the overall appearance recognition can be recognition of the type, color and the like of the vehicle. In the embodiment of the invention, the local identification and the overall appearance identification are realized by a convolutional neural network.
In detail, in an embodiment of the present invention, the S2 includes:
step a, capturing license plate information in the target frame through a pre-constructed license plate recognition algorithm, and classifying the target frame according to the license plate information;
and b, when the license plate information cannot be captured, extracting the features of the vehicle through a pre-constructed feature extraction model, and classifying the target frame by using the extracted features.
The license plate recognition algorithm is based on license plate textures in the target frame, a license plate positioning preprocessing model based on directed fractal parameters is established by applying a separation theory, a license plate region is extracted by combining a projection method, characters are segmented and recognized, and a result is output finally.
The feature extraction in the feature extraction model is a concept in computer vision and image processing, and means that image information is extracted by using a computer to determine whether a point of each image belongs to an image feature. The embodiment of the invention can extract the characteristics of the vehicle through the characteristic extraction model, such as: the vehicle type, the color, the driving direction and the like, and when the license plate information cannot be identified, the vehicle is identified.
In detail, in the embodiment of the present invention, the step b includes:
primarily classifying the target frame according to the appearance and color characteristics of the vehicle; and
and performing secondary classification on the basis of the primary classification according to the traveling track of the vehicle.
According to the embodiment of the invention, after the characteristic extraction model identifies the vehicle according to the color and the vehicle type, the vehicles with the same color and the same vehicle type in different driving directions are excluded according to the traveling track of the vehicle, so that the accurate identification of the vehicle can be realized.
S3, carrying out tracking inspection on each target object to obtain a tracking track of each target object, and combining the tracking tracks to obtain a tracking track set.
According to the embodiment of the invention, the positions of the target objects in the continuous shooting images are captured and numbered, the positions and the corresponding numbers are extracted into one picture, and the positions in the picture are connected according to the sequence of the numbers to generate the tracking track of the vehicle.
And S4, carrying out violation analysis on each tracking track in the tracking track set to obtain violation targets and corresponding tracking tracks.
In detail, in an embodiment of the present invention, the S4 includes:
identifying traffic element information in the target object picture set, and generating a compliant driving area of the target object by using the traffic element information; judging the driving compliance of the target object according to whether the tracking track exceeds the range of the compliant driving area; and when the driving of the target object is not in compliance, extracting the target object and the target object picture set corresponding to the target object.
In the embodiment of the invention, a compliant driving area is constructed for each target object through the traffic element information, and when the target object exceeds the compliant driving area, the violation of the target object is judged.
In detail, the traffic element information mainly includes: lane lines, etc.
Further, in the embodiment of the present invention, the identifying the traffic element information in the target object picture set includes:
preprocessing the target object picture set by utilizing a super-resolution reconstruction technology; segmenting the road information of the preprocessed target object picture set, and extracting a lane line to obtain a lane line identifier; and classifying the lane line identification to obtain the traffic element information in the target object picture set.
The super-resolution reconstruction is a process of obtaining a high-resolution image from a single or a plurality of low-resolution images. The super-resolution reconstruction may employ an interpolation-based method, a reconstruction-based method, or a learning-based method. According to the embodiment of the invention, the pixel information at a longer distance in the picture can be reconstructed by the super-resolution reconstruction method.
The super-resolution reconstruction based on interpolation, reconstruction and learning methods is the prior art and is not described here.
Further, when the pre-processed target object picture set is segmented by the pre-constructed Unet structure network, in order to overcome the influence of unstable factors such as illumination and shielding on the segmentation effect, the segmentation process is divided into two stages, wherein in the first stage, only whether the target object picture set is a lane line is distinguished, and the types of the lane lines (yellow solid lines, yellow dotted lines, white solid lines and white dotted lines) and the diversion lines are not distinguished. And in the second stage, distinguishing the lane lines according to a pre-constructed lane line feature extraction model, judging the types of the lane lines, and performing supplementary drawing on the original lane lines according to the types of the lane lines. And when the tracking track of the vehicle touches a special lane line, extracting the corresponding continuous shooting image as an evidence image.
And S5, storing the violation target object and the corresponding tracking track into a violation database.
According to the embodiment of the invention, the violation targets and the corresponding tracking tracks can be stored in the pre-constructed violation database through distributed storage, and the distributed storage can increase the space utilization rate of the storage space and ensure the information safety.
According to the embodiment of the invention, the target frame is obtained by obtaining the target object picture set and carrying out target object position detection on the target object picture set, and the preset number of pictures are analyzed to replace the video, so that the consumption of computing and storing resources can be reduced. By means of the local identification and/or overall appearance identification method and tracking and checking of each target object, vehicle information and tracking tracks can be completely controlled, and the problem of monitoring failure caused by traffic jam and mutual shielding is avoided. According to the embodiment of the invention, the violation target and the corresponding tracking track are stored in the violation database, so that the violation evidence can be stored at the first time when the traffic violation is found, and the violation evidence obtaining efficiency is increased. Therefore, the embodiment of the invention can improve the violation monitoring recognition rate and the monitoring efficiency and reduce the computing resources consumed by monitoring.
To ensure data security, the offending targets and corresponding trace tracks may be stored in a blockchain.
Fig. 2 is a schematic block diagram of the device for monitoring violation of target object according to the present invention.
The device 100 for monitoring violation of target object according to the present invention may be installed in an electronic device. According to the realized functions, the device 100 for monitoring violation of target object may include a target object detection module 101, a target object classification module 102, a target object violation monitoring module 103, and an evidence storage module 104. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the target object detection module 101 is configured to obtain a target object picture set, and perform target object position detection on the target object picture set to obtain a target frame.
The embodiment of the invention can shoot the picture set of the target object through any video or image shooting equipment. Continuously shooting vehicles running in a preset section to obtain a preset number of continuously shot images, and forming the preset number of continuously shot images into the target object picture set.
According to the embodiment of the invention, a monitoring camera fixed at a traffic intersection can be utilized to continuously shoot the vehicles from appearance to disappearance when the vehicles pass through, photos with the same time interval of preset Q number are selected to form the target object picture set, and Q can be 4.
Further, the target object in the embodiment of the present invention is mainly a target object that may violate a traffic regulation, and may be a vehicle or a person. Preferably, in the embodiment of the present invention, the target object is a vehicle traveling in a preset section. The target frame is a kind of identification pattern that highlights the target.
In another embodiment of the present invention, the target detection module 101 may further be configured to:
obtaining a vehicle judgment model by utilizing a classification network which is pre-constructed by training a preset number of normal vehicles and special vehicles;
and identifying a special vehicle from the target frame by using the vehicle judgment model, and deleting the target frame corresponding to the special vehicle.
The classification network of the embodiment of the invention can be a MobileNet V3 classification network.
The MobileNetV3 classification network can be divided into a start part, a middle part, and a last part. The initial part comprises 1 convolutional layer, and features are extracted through convolution of 3x 3; the middle part is a network structure of a plurality of blocks (MobileBlock) containing convolutional layers; the final part outputs the category by two 1x1 convolutional layers instead of full connection.
In detail, the embodiment of the invention carries out classification training on the MobileNet 3 classification network by collecting and labeling the training data of 18 types of vehicles including normal vehicles and special vehicles, and judges whether the vehicles belong to the special vehicles. Preferably, the special vehicle may include, but is not limited to, police cars, ambulances, city sweepers, and the like.
The object classification module 102 is configured to classify the object frames by a local recognition method and/or an overall appearance recognition method, and recognize each object according to a classification result.
In the embodiment of the invention, the local recognition can be license plate recognition and the overall appearance recognition can be recognition of the type, color and the like of the vehicle. In the embodiment of the invention, the local identification and the overall appearance identification are realized by a convolutional neural network.
In detail, in the embodiment of the present invention, the target object classification module 102 classifies the target frame by the following method:
capturing license plate information in the target frame through a pre-constructed license plate recognition algorithm, and classifying the target frame according to the license plate information;
and when the license plate information cannot be captured, performing feature extraction on the vehicle through a pre-constructed feature extraction model, and classifying the target frame by using the extracted features.
The license plate recognition algorithm is based on license plate textures in the target frame, a license plate positioning preprocessing model based on directed fractal parameters is established by applying a separation theory, a license plate region is extracted by combining a projection method, characters are segmented and recognized, and a result is output finally.
The feature extraction in the feature extraction model is a concept in computer vision and image processing, and means that image information is extracted by using a computer to determine whether a point of each image belongs to an image feature. The embodiment of the invention can extract the characteristics of the vehicle through the characteristic extraction model, such as: the vehicle type, the color, the driving direction and the like, and when the license plate information cannot be identified, the vehicle is identified.
In detail, in the embodiment of the present invention, the extracting features of the vehicle through the pre-constructed feature extraction model, and classifying the target frame by using the extracted features includes:
primarily classifying the target frame according to the appearance and color characteristics of the vehicle; and
and performing secondary classification on the basis of the primary classification according to the traveling track of the vehicle.
After the target object classification module 102 performs vehicle identification according to colors and vehicle types through the feature extraction model, vehicles with the same color and the same vehicle type in different driving directions are further excluded according to the traveling track of the vehicle, so that accurate identification of the vehicle can be realized.
The target violation monitoring module 103 is configured to perform tracking inspection on each target to obtain a tracking track of each target, combine the tracking tracks to obtain a tracking track set, and perform violation analysis on each tracking track in the tracking track set to obtain a violation target and a corresponding tracking track.
According to the embodiment of the invention, the positions of the target objects in the continuous shooting images are captured and numbered, the positions and the corresponding numbers are extracted into one picture, and the positions in the picture are connected according to the number sequence to generate the tracking track of the vehicle.
In detail, in the embodiment of the present invention, when performing violation analysis on each tracking track in the tracking track set to obtain a violation target and a corresponding tracking track, the target violation monitoring module 103 performs the following operations:
identifying traffic element information in the target object picture set, and generating a compliant driving area of the target object by using the traffic element information; judging the driving compliance of the target object according to whether the tracking track exceeds the range of the compliant driving area; and when the driving of the target object is not in compliance, extracting the target object and the target object picture set corresponding to the target object.
In the embodiment of the invention, a compliant driving area is constructed for each target object through the traffic element information, and when the target object exceeds the compliant driving area, the violation of the target object is judged.
In detail, the traffic element information mainly includes: lane lines, etc.
Further, in the embodiment of the present invention, the identifying the traffic element information in the target object picture set includes:
preprocessing the target object picture set by utilizing a super-resolution reconstruction technology; segmenting the road information of the preprocessed target object picture set, and extracting a lane line to obtain a lane line identifier; and classifying the lane line identification to obtain the traffic element information in the target object picture set.
The super-resolution reconstruction is a process of obtaining a high-resolution image from a single or a plurality of low-resolution images. The super-resolution reconstruction may employ an interpolation-based method, a reconstruction-based method, or a learning-based method. According to the embodiment of the invention, the pixel information at a longer distance in the picture can be reconstructed by the super-resolution reconstruction method.
The super-resolution reconstruction based on interpolation, reconstruction and learning methods is the prior art and is not described here.
Further, when the pre-processed target object picture set is segmented by the pre-constructed Unet structure network, in order to overcome the influence of unstable factors such as illumination and shielding on the segmentation effect, the segmentation process is divided into two stages, wherein in the first stage, only whether the target object picture set is a lane line is distinguished, and the types of the lane lines (yellow solid lines, yellow dotted lines, white solid lines and white dotted lines) and the diversion lines are not distinguished. And in the second stage, distinguishing the lane lines according to a pre-constructed lane line feature extraction model, judging the types of the lane lines, and performing supplementary drawing on the original lane lines according to the types of the lane lines. And when the tracking track of the vehicle touches a special lane line, extracting the corresponding continuous shooting image as an evidence image.
The evidence storage module 104 is configured to store the violation target and the corresponding tracking trajectory in a violation database.
The evidence storage module 104 may store the violation targets and the corresponding tracking tracks in a pre-constructed violation database through distributed storage, which may increase the space utilization of the storage space and ensure information security.
Fig. 3 is a schematic structural diagram of an electronic device for implementing the method for monitoring violation of target object according to the present invention.
The electronic device 1 may include a processor 10, a memory 11, and a bus, and may further include a computer program, such as an object violation monitoring program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used to store not only application software installed in the electronic device 1 and various types of data, such as codes of the object violation monitoring program 12, but also temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (e.g., executing a target violation monitoring program, etc.) stored in the memory 11 and calling data stored in the memory 11.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The object violation monitoring program 12 stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, which when executed in the processor 10, can implement:
acquiring a target object picture set, and carrying out target object position detection on the target object picture set to obtain a target frame;
classifying the target frames by a local identification method and/or an overall appearance identification method, and identifying each target object according to a classification result;
performing tracking inspection on each target object to obtain a tracking track of each target object, and combining the tracking tracks to obtain a tracking track set;
carrying out violation analysis on each tracking track in the tracking track set to obtain violation targets and corresponding tracking tracks;
and storing the violation target object and the corresponding tracking track into a violation database.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any accompanying claims should not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for monitoring violation of a target, the method comprising:
acquiring a target object picture set, and carrying out target object position detection on the target object picture set to obtain a target frame;
classifying the target frames by a local identification method and/or an overall appearance identification method, and identifying each target object according to a classification result;
performing tracking inspection on each target object to obtain a tracking track of each target object, and combining the tracking tracks to obtain a tracking track set;
carrying out violation analysis on each tracking track in the tracking track set to obtain violation targets and corresponding tracking tracks;
and storing the violation target object and the corresponding tracking track into a violation database.
2. The method for monitoring violation of objects according to claim 1, wherein said obtaining a set of images of an object comprises:
continuously shooting vehicles running in a preset section to obtain a preset number of continuously shot images, and forming the preset number of continuously shot images into the target object picture set.
3. The method for monitoring object violation according to claim 2, wherein the classifying the object frame by a local recognition and/or a global appearance recognition method comprises:
capturing license plate information in the target frame through a pre-constructed license plate recognition algorithm, and classifying the target frame according to the license plate information;
and when the license plate information cannot be captured, performing feature extraction on the vehicle through a pre-constructed feature extraction model, and classifying the target frame by using the extracted features.
4. The method for monitoring object violation according to claim 3, wherein the step of performing feature extraction on the vehicle through a pre-constructed feature extraction model and classifying the object frame by using the extracted features comprises the steps of:
primarily classifying the target frame according to the appearance and color characteristics of the vehicle;
and performing secondary classification on the basis of the primary classification according to the traveling track of the vehicle.
5. The method for monitoring violation of objects according to claim 2, wherein the obtaining of the image set of the object, the detecting of the position of the object on the image set of the object, and the obtaining of the object frame further comprise:
obtaining a vehicle judgment model by utilizing a classification network which is pre-constructed by training a preset number of normal vehicles and special vehicles;
and identifying a special vehicle from the target frame by using the vehicle judgment model, and deleting the target frame corresponding to the special vehicle.
6. The method for monitoring violation of target objects according to any one of claims 2-4, wherein the performing violation analysis on each tracking trajectory in the set of tracking trajectories to obtain the violation target object and the corresponding tracking trajectory comprises:
identifying traffic element information in the target object picture set, and generating a compliant driving area of the target object by using the traffic element information;
judging the driving compliance of the target object according to whether the tracking track exceeds the range of the compliant driving area;
and when the running of the target object is not in compliance, extracting the target object and a target object picture set corresponding to the target object to obtain the illegal target object and a corresponding tracking track.
7. The method for monitoring violation of objects according to claim 6, wherein said identifying traffic element information in said set of images of objects comprises:
preprocessing the target object picture set by utilizing a super-resolution reconstruction technology;
segmenting the road information of the preprocessed target object picture set, and extracting a lane line to obtain a lane line identifier;
and classifying the lane line identification to obtain the traffic element information in the target object picture set.
8. An object violation monitoring device, comprising:
the target object detection module is used for acquiring a target object picture set and carrying out target object position detection on the target object picture set to obtain a target frame;
the target object classification module is used for classifying the target frames through a local identification method and/or an overall appearance identification method, and identifying each target object according to a classification result;
the target violation monitoring module is used for tracking and checking each target to obtain a tracking track of each target, combining the tracking tracks to obtain a tracking track set, and carrying out violation analysis on each tracking track in the tracking track set to obtain a violation target and a corresponding tracking track;
and the evidence storage module is used for storing the violation target object and the corresponding tracking track into a violation database.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform a method for target violation monitoring as recited in any one of claims 1-7.
10. A computer-readable storage medium comprising a storage data area and a storage program area, wherein the storage data area stores created data, and the storage program area stores a computer program; wherein the computer program, when executed by a processor, implements a target violation monitoring method according to any one of claims 1-7.
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