CN111754786A - System for identifying traffic vehicle passing events on highway - Google Patents
System for identifying traffic vehicle passing events on highway Download PDFInfo
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- CN111754786A CN111754786A CN202010679575.6A CN202010679575A CN111754786A CN 111754786 A CN111754786 A CN 111754786A CN 202010679575 A CN202010679575 A CN 202010679575A CN 111754786 A CN111754786 A CN 111754786A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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Abstract
The invention discloses a system for identifying traffic events of highway vehicles, which belongs to the technical field of visual image detection data processing and comprises an image acquisition module, an intelligent front-end module, a transmission module and a control center module; the system collects data by using the existing camera of the highway, analyzes and processes the obtained data after intelligent front-end deep learning, judges road conditions according to customized rules to obtain data of traffic flow, traffic jam, traffic accidents, illegal parking, illegal driving and the like, transmits the data to the highway control center by the transmission module, and classifies, displays and stores the received data by the control center. The system is based on the expressway storage cameras, does not need to be arranged, can be deployed quickly, and saves manpower and material resources; the whole analysis and report of the driving event information are directly and automatically completed by the system, manual processing is not needed, the result display is fast, the report is timely, and the intelligent driving event information reporting system is more intelligent.
Description
Technical Field
The invention relates to the technical field of visual image detection data processing, in particular to a system for identifying a traffic incident of a highway.
Background
In modern traffic, real-time and accurate control of road conditions is very important for improving traffic efficiency, reducing congestion and avoiding traffic accidents. Therefore, how to obtain accurate traffic state information has become a general concern for a large number of traffic participants or managers. The accurate traffic state information can provide support for traffic decision analysis, and meanwhile, efficient, safe and comfortable traffic transportation service can be provided for travelers, so that the utilization efficiency of traffic resources is improved, the energy consumption is reduced, and the urban economy is promoted to develop more quickly and stably.
At present, the judgment of the road traffic running state mostly adopts a manual judgment method, which comprises the following steps: citizen reports, full-time staff reports, civil radio, closed circuit television surveillance, aviation surveillance, and the like.
In addition, a more common method is a method for judging a traffic state by using traffic flow parameters obtained by a traffic detector. Currently, the average speed of traffic flow in a section of road is generally used for judgment, and if the average speed is greater than or equal to 40 kilometers per hour, the road is green; red if the average speed is less than 20 km/h; otherwise yellow, however, both thresholds may be modified.
The currently used manual judgment method requires witnesses in the local at present, continuous observation is needed, special personnel are needed to screen and confirm reports, and the workload and the intensity of the personnel are large; the judgment is carried out by using the traffic flow parameters, and the method also has unreasonable points: the statistical period, the state of each road section, the distribution of the detectors and the like have influence on the state judgment, the traffic conditions of each road section are different, the distribution positions of the detectors of each road section are different, and the traffic state information obtained by the method is lack of accuracy.
Disclosure of Invention
It is an object of the present invention to overcome the above-mentioned background difficulties and to provide a system for identifying highway vehicle traffic events.
In order to achieve the purpose, the technical scheme is as follows: a system for identifying a highway vehicle transit event comprising the following modules: the system comprises an image acquisition module, an intelligent front-end module, a transmission module and a control center module; the functions of the modules are as follows:
an image acquisition module: acquiring traffic video data by using the existing camera of the expressway, acquiring an original sampling image, and obtaining the driving condition of a road in a sampling area;
intelligent front end module: the method is characterized in that an intelligent front end is installed on an existing camera of the highway, the intelligent front end is an intelligent device provided with an analysis system, and the intelligent front end is used for processing collected traffic video data and comprises the following steps:
s1, extracting a candidate region of the acquired image by an intelligent front end, sharpening the candidate region and converting the sharpened candidate region into an image with a fixed size; s2, training by using the convolutional neural network model as a machine learning model to obtain a model for identifying types of vehicles such as cars and trucks; s3, the intelligent front end performs feature learning on the image with the fixed size by using a convolutional neural network, and classifies the learned features to obtain a deep learning model; s4, the intelligent front end automatically detects the type of the vehicle and records the track of the vehicle; s5, judging the driving road conditions by the intelligent front end, and tracking the driving tracks of various vehicles to further obtain traffic road conditions such as traffic flow, traffic jam, driving accidents, illegal parking, illegal driving and the like;
a transmission module: transmitting the data processed by the intelligent front end to a control center;
a control center module: and displaying and storing the received data.
Further, the traffic flow in step S4 of the intelligent front-end module is calculated as: a section is dynamically defined in the sampling area, and when the vehicle object is detected to completely pass through the section, the vehicle flow is considered to be accumulated.
Further, the traffic jam determination in the step S4 of the intelligent front-end module is as follows: firstly, judging whether a certain vehicle target is a congestion unit or not in a sampling area; then, a congestion area is detected, and if there are more than a predetermined number of congestion units in the congestion detection area, the area is determined to be a congestion area.
Further, the intelligent front-end module judges the driving accident in the step S4: in the sampling area, target vehicles meeting the congestion condition and target vehicles not meeting the congestion condition exist at the same time, or the number of the target vehicles is small and the congestion condition exists, and at the moment, the condition is judged to be met with the accident condition.
Further, it is characterized in that: and the intelligent front-end module judges whether the vehicle is parked in a violation manner or driven in a violation manner in the step S4: in the sampling area, dynamically observing the monitoring area at the cloud end, defining an area as a forbidden area and a forbidden area, and judging that the illegal conditions are met when a monitored target enters the area; when it is monitored that a target enters the area for a certain time, the condition of illegal parking is judged to be met.
The beneficial effect who adopts above-mentioned scheme does: the system for identifying the traffic incident of the highway vehicle is based on the highway storage cameras, does not need to be arranged, can be rapidly deployed, and saves manpower and material resources; the whole analysis and report of the driving event information are directly and automatically completed by the system, manual processing is not needed, the result display is fast, the report is timely, and the intelligent driving event information reporting system is more intelligent.
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FIG. 1 is a block diagram of a system for identifying highway vehicle traffic events in accordance with the present invention.
FIG. 2 is a flow chart of a system for identifying highway vehicle traffic events in accordance with the present invention.
Detailed Description
The technical solution of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention. Referring to the drawings, like numbers indicate like or similar elements throughout the views. The described embodiments are only some, not all embodiments of the invention. 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.
Referring to fig. 1 and 2, a system for identifying a traffic incident of a highway vehicle comprises an image acquisition module, an intelligent front-end module, a transmission module and a control center module; the functions of the modules are as follows:
an image acquisition module: acquiring traffic video data by using the existing camera of the expressway, acquiring an original sampling image, and obtaining the driving condition of a road in a sampling area;
intelligent front end module: the method is characterized in that an intelligent front end is installed on an existing camera of the highway, the intelligent front end is an intelligent device provided with an analysis system, and the intelligent front end is used for processing collected traffic video data and comprises the following steps:
1. the intelligent front end extracts candidate regions of the acquired image, sharpens the candidate regions and converts the sharpened candidate regions into an image with a fixed size;
2. training by using a convolutional neural network model as a machine learning model to obtain a model for identifying types of vehicles such as cars and trucks;
3. the intelligent front end performs feature learning on the image with the fixed size by using a convolutional neural network, and classifies the learned features to obtain a deep learning model;
4. the intelligent front end automatically detects the type of the vehicle and records the track of the vehicle;
5. the intelligent front end judges the driving road conditions, and obtains traffic road conditions such as traffic flow, driving congestion, driving accidents, illegal parking, illegal driving and the like by tracking the driving tracks of various vehicles;
5.1. and (3) calculating the traffic flow: and dynamically defining a section in the sampling area, when a vehicle target is monitored to completely pass through the section, the section is regarded as the traffic flow accumulation, the data is reported once in unit time, and the background counts the traffic flow in unit time or in a plurality of unit times.
5.2. Judging traffic jam: firstly, in a sampling area, judging whether a certain vehicle target is a congestion unit, namely, tracking the stay time T of a target vehicle C in the sampling areaduring≥TthresholdThen the target is considered to be a congestion unit, where TthresholdThe value is a preset adjustable value, the unit is second, and the value range is more than or equal to 1; residence time Tduring=Tcur-Cinit_time(wherein T iscurFor the current video frame time, Cinit_timeInitial time for tracking target vehicle C);
for example: setting TthresholdIs 2 seconds, tracking the initial time C of the target vehicle Cinit_time11:00:00, current video frame time TcurAt 11:00:05, the residence time Tduring= Tcur-Cinit_time=5 seconds, at which time the dwell time T isduring≥TthresholdThen, the target vehicle C is determined to be a congestion unit.
Secondly, detecting a congestion area if N exists in the same lane in the congestion detection areathreshold_perloadIndividual congestion units, or total presence of N in the areathreshold_totalThe congestion unit judges that the area is a congestion area; wherein N isthreshold_perloadAnd Nthreshold_totalThe preset adjustable values are all larger than or equal to 1;
for example: setting Nthreshold_perloadA value of 50, Nthreshold_totalThe value is 150, and when the number of congestion units in the same lane in the detection area reaches 50 or the total number of congestion units in the detection area reaches 150, the area is judged to be a congestion area.
And (3) multi-stage congestion judgment: detecting area congestion or the presence of N if the current congestion isthresholdAnd entering the next congestion detection area for congestion judgment according to the congestion target, wherein the set of congestion areas needing to be reported is P = { area =iI < n }; wherein, areaiIndicating the ith congestion detectionDetecting areas, wherein the congestion areas are numbered from front to back according to the driving direction, and n is the number of the congestion area at the last detected position; n is a radical ofthresholdThe value range is more than or equal to 1 for the preset adjustable value.
For example: setting Nthreshold50, when the congestion target of the detection area reaches 50, the area is judged as the first area1A congested area and area1And combining the joining with the P, then entering the next congestion detection area for congestion judgment until n congestion detection areas are detected, joining the detection areas meeting the conditions into a set P, and finally reporting the set P.
5.3. Judging a driving accident: in the sampling area, target vehicles meeting the congestion condition and target vehicles not meeting the congestion condition exist at the same time, or the target vehicles are few and the congestion condition exists, and the condition is judged to be met with the accident condition.
5.4. And (3) illegal parking and driving judgment: in the sampling area, dynamically observing the monitoring area at the cloud end, defining an area as a forbidden area and a forbidden area, and judging that the illegal conditions are met when a monitored target enters the area; when it is monitored that a target enters the area for a certain time, the condition of illegal parking is judged to be met.
A transmission module: and transmitting the data processed by the intelligent front end to the highway control center.
A control center module: the received data are classified, displayed and stored, and an alarm device is set according to the emergency degree of different events so as to remind the watchman to rapidly process the data.
The system collects data by using the existing camera of the highway, analyzes and processes the obtained data after intelligent front-end deep learning, judges road conditions according to customized rules to obtain data of traffic flow, traffic jam, traffic accidents, illegal parking, illegal driving and the like, transmits the data to the highway control center by the transmission module, and classifies, displays and stores the received data by the control center. The system is based on the expressway storage cameras, does not need to be arranged, can be deployed quickly, and saves manpower and material resources; the whole analysis and report of the driving event information are directly and automatically completed by the system, manual processing is not needed, the result display is fast, the report is timely, and the intelligent driving event information reporting system is more intelligent.
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 reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (5)
1. A system for identifying highway vehicle traffic events, characterized by: the system comprises the following modules: the system comprises an image acquisition module, an intelligent front-end module, a transmission module and a control center module; the functions of the modules are as follows:
an image acquisition module: acquiring traffic video data by using the existing camera of the expressway, acquiring an original sampling image, and obtaining the driving condition of a road in a sampling area;
intelligent front end module: the method is characterized in that an intelligent front end is installed on an existing camera of the highway, the intelligent front end is an intelligent device provided with an analysis system, and the intelligent front end is used for processing collected traffic video data and comprises the following steps:
s1, extracting a candidate region of the acquired image by an intelligent front end, sharpening the candidate region and converting the sharpened candidate region into an image with a fixed size; s2, training by using the convolutional neural network model as a machine learning model to obtain a model for identifying types of vehicles such as cars and trucks; s3, the intelligent front end performs feature learning on the image with the fixed size by using a convolutional neural network, and classifies the learned features to obtain a deep learning model; s4, the intelligent front end automatically detects the type of the vehicle and records the track of the vehicle; s5, judging the driving road conditions by the intelligent front end, and tracking the driving tracks of various vehicles to further obtain traffic road conditions such as traffic flow, traffic jam, driving accidents, illegal parking, illegal driving and the like;
a transmission module: transmitting the data processed by the intelligent front end to a control center;
a control center module: and displaying and storing the received data.
2. The system for identifying highway vehicle passing events according to claim 1, wherein: the traffic flow in step S4 of the intelligent front-end module is calculated as: a section is dynamically defined in the sampling area, and when the vehicle object is detected to completely pass through the section, the vehicle flow is considered to be accumulated.
3. The system for identifying highway vehicle passing events according to claim 1, wherein: and the traffic jam judgment of the intelligent front-end module in the step S4 is as follows: firstly, judging whether a certain vehicle target is a congestion unit or not in a sampling area; then, a congestion area is detected, and if there are more than a predetermined number of congestion units in the congestion detection area, the area is determined to be a congestion area.
4. The system for identifying highway vehicle passing events according to claim 1, wherein: and judging the driving accident in the step S4 of the intelligent front-end module: in the sampling area, target vehicles meeting the congestion condition and target vehicles not meeting the congestion condition exist at the same time, or the number of the target vehicles is small and the congestion condition exists, and at the moment, the condition is judged to be met with the accident condition.
5. The system for identifying highway vehicle passing events according to claim 1, wherein: and the intelligent front-end module judges whether the vehicle is parked in a violation manner or driven in a violation manner in the step S4: in the sampling area, dynamically observing the monitoring area at the cloud end, defining an area as a forbidden area and a forbidden area, and judging that the illegal conditions are met when a monitored target enters the area; when it is monitored that a target enters the area for a certain time, the condition of illegal parking is judged to be met.
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Cited By (5)
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| CN113606521A (en) * | 2021-08-04 | 2021-11-05 | 深圳市英特飞电子有限公司 | Multifunctional intelligent street lamp |
| CN114220259A (en) * | 2021-08-13 | 2022-03-22 | 苏交科集团股份有限公司 | Expressway emergency control method based on data fusion |
| CN114566058A (en) * | 2022-04-30 | 2022-05-31 | 山东通维信息工程有限公司 | Management method and system for operation and maintenance monitoring data of highway |
| CN114973659A (en) * | 2022-05-12 | 2022-08-30 | 山东高速集团有限公司创新研究院 | Method, device and system for detecting indirect event of expressway |
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