CN111951576A - Traffic light control system based on vehicle identification and method thereof - Google Patents
Traffic light control system based on vehicle identification and method thereof Download PDFInfo
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- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/081—Plural intersections under common control
- G08G1/083—Controlling the allocation of time between phases of a cycle
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Abstract
The application discloses traffic light control system and method based on vehicle identification, the system includes: the video monitoring sites are accessed to a public network through a base station and are in communication connection with an exchanger of a remote monitoring center, the exchanger is connected with a hard disk video recorder and a monitoring client, and the hard disk video recorder is connected with a large monitoring screen; the controller of the video monitoring station is also connected with a first 4G/5G wireless router, the front end of the video monitoring station is also provided with an RFID vehicle counting station, and the RFID vehicle counting station comprises an RFID reader, an RFID antenna connected with the RFID reader and a second 4G/5G wireless router in wireless communication connection with the first 4G/5G wireless router. The method comprises the steps of collecting images through a camera, processing the images and detecting movement, counting vehicles through an RFID reader, calculating the length of a pre-passing vehicle queue, calculating out queue passing time, and controlling the green light lighting time according to the calculated queue passing time, so that the reasonable configuration of traffic flow control is realized, and the urban traffic management efficiency is improved.
Description
Technical Field
The application relates to the technical field of traffic control, in particular to a traffic light control system and a traffic light control method based on vehicle identification.
Background
In order to solve the problem of road traffic, conventional traffic solutions, such as widening roads, increasing road network density, establishing three-dimensional traffic, etc., increasingly show their limitations, and only by using high-tech means, increasingly serious traffic problems can be improved, and therefore, the introduction and development of intelligent traffic becomes a necessary trend of traffic development.
At present, most urban traffic lights in China adopt a fixed period mode, namely the period of the traffic lights is fixed and unchangeable, and the time of the traffic lights in a few cities can be controlled by a traffic manager through remote control. The fixed period of the traffic signal lamp brings about several problems: (1) vehicles are in one direction or are in a plurality of directions and are red light currently, so that the vehicles cannot pass, and vehicles are not in the other direction or are relatively few and are green light currently, so that the vehicles do not pass on the road surface after passing, and the vehicles in the other direction need to pass and have to wait for the arrival of the green light, thereby causing time waste. (2) The above situation causes time waste and economic loss, because the automobile cannot walk and the engine cannot be shut down, the automobile consumes oil in the waiting condition, which causes economic loss, and because the oil is not completely combusted in the waiting condition, toxic gas (such as sulfur dioxide) is discharged more than that in normal running. (3) When the traffic flow is very large at a certain moment (such as the time of going to work or leaving work), the time of the traffic signal lamp is fixed and cannot be prolonged, so that the switching of the traffic signal lamp is too fast, and the passing efficiency of vehicles at the crossroad is low. Therefore, how to apply the intelligent traffic system to the traffic intersection and how to intelligentize the traffic light control system becomes the key for solving the problem of traffic intersection congestion.
Disclosure of Invention
The application aims to provide a traffic light control system based on vehicle identification and a method thereof, and aims to solve the problems that the existing traffic light control system is fixed in control time, causes time waste, economic loss and environmental pollution, cannot prolong the time of traffic lights, and is switched too fast, so that the passing efficiency of vehicles at a crossroad is low, and traffic jam is easily caused.
The application is implemented by the following technical scheme: a traffic light control system based on vehicle identification comprises a plurality of video monitoring stations arranged at a plurality of traffic crossroads, wherein each video monitoring station comprises a camera, a controller and a traffic light, the cameras, the controllers and the traffic lights are sequentially connected, the cameras and the controllers are connected with a power module, the video monitoring stations are accessed into a public network through a base station and are in communication connection with an exchanger of a remote monitoring center, the exchanger is connected with a hard disk video recorder and a monitoring client, and the hard disk video recorder is connected with a monitoring large screen; the controller of the video monitoring station is further connected with a first 4G/5G wireless router, the front end of the video monitoring station is further provided with an RFID vehicle counting station, the RFID vehicle counting station comprises an RFID reader, an RFID antenna and a second 4G/5G wireless router, the RFID antenna and the second 4G/5G wireless router are connected with the RFID reader, and the first 4G/5G wireless router and the second 4G/5G wireless router are in wireless communication connection.
Preferably, the controller is any one of an ARM processor, a DSP or an FPGA.
Preferably, the monitoring client is a computer.
Preferably, the power module is a solar panel.
A traffic light control method based on vehicle identification comprises the following steps:
acquiring an image through a camera, preprocessing the image, extracting features of the preprocessed image, and detecting vehicle motion;
counting the passing vehicles between the RFID reader and the traffic intersection through the RFID reader arranged at the front section of the traffic intersection;
combining the motion detection result and the passing vehicle counting result to calculate the length of the pre-passing vehicle queue;
and calculating the queue passing time according to the length of the pre-passing vehicle queue, and controlling the green light lighting time according to the calculated queue passing time.
Preferably, the preprocessing the image sequentially comprises image graying, image smoothing filtering and image registration; the image smoothing filtering includes any one of mean filtering, weighted average filtering, or median filtering.
Preferably, the feature extraction of the preprocessed image includes:
establishing a masking layout with the same size and the same channel number as the image to be processed and the gray values of all 0 (namely black);
the method comprises the steps of defining an interested area in an image to be processed, and simultaneously recording coordinates of four vertexes of an irregular quadrangle;
taking the four pixel points at the same coordinate of the mask image, and connecting the four pixel points into a closed area;
assigning the pixel points in the closed area to be 255 (namely white), and finishing the mask making;
and normalizing the mask pixel value, and then performing phase comparison with the image to be processed to extract a characteristic region.
Preferably, the detecting the motion of the vehicle on the image after the feature extraction includes detecting the motion of the vehicle by using a frame difference method.
Preferably, the counting of the passing vehicles between the RFID reader and the traffic intersection includes using a passing counting method.
Preferably, the calculating of the queue passing time according to the length of the queue of the pre-passing vehicles includes: the number of vehicles in the road section is set as i +1, the running speed is set as vm/s, and the position of the vehicles in the road section is set asxi ═ l ═ u, where u is [0, 1 ═ u]The random number is uniformly distributed in the interval, l is the length of the pre-passing vehicle queue, and the estimated time for the vehicle to normally pass through the road section is
The application has the advantages that:
the method comprises the steps of collecting images through a camera, preprocessing the images, carrying out feature extraction on the preprocessed images, carrying out vehicle motion detection, reading a device through an RFID arranged at the front section of a traffic intersection, counting the passing vehicles between the RFID reader and the traffic intersection, combining a motion detection result and a passing vehicle counting result, calculating the length of a pre-passing vehicle queue, calculating the passing time of the queue according to the length of the pre-passing vehicle queue, and controlling the green light lighting time according to the calculated queue passing time. The time of the traffic lights is controlled, and based on the actual condition setting of the vehicle queue in front of the crossroad monitored by combining the video monitoring with the RFID identification counting, the reasonable configuration of traffic flow control is realized, and the urban traffic management efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application 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 application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of a system according to an embodiment of the present application.
Fig. 2 is a schematic block diagram of a video surveillance site and an RFID vehicle counting site according to an embodiment of the present application.
Fig. 3 is a flow chart of a method according to an embodiment of the present application.
Fig. 4 is a schematic field structure diagram of a monitoring system according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
As shown in fig. 1 and 2, a traffic light control system based on vehicle identification comprises a plurality of video monitoring stations arranged at a plurality of traffic crossroads, wherein each video monitoring station comprises a camera, a controller and a traffic light, the cameras, the controllers and the traffic lights are sequentially connected, the cameras and the controllers are connected with a power supply module, the video monitoring stations are accessed into a public network through a base station and are in communication connection with an exchanger of a remote monitoring center, the exchanger is connected with a hard disk video recorder and a monitoring client, and the hard disk video recorder is connected with a monitoring large screen; the controller of the video monitoring station is also connected with a first 4G/5G wireless router, the front end of the video monitoring station is also provided with an RFID vehicle counting station, the RFID vehicle counting station comprises an RFID reader, an RFID antenna and a second 4G/5G wireless router, the RFID antenna and the second 4G/5G wireless router are connected with the RFID reader, and the first 4G/5G wireless router and the second 4G/5G wireless router are in wireless communication connection.
In this embodiment, the controller is any one of an ARM processor, a DSP, or an FPGA.
In this embodiment, the monitoring client is a computer, and the video status of vehicles at each traffic intersection and the vehicle counting status at the front end of the traffic intersection can be remotely monitored in real time through the client computer or the large monitoring screen.
In this embodiment, power module is solar cell panel, through the power supply of solar cell panel, make full use of solar energy, and energy-concerving and environment-protective, and avoided the problem that urban traffic wiring is complicated, with high costs.
Referring to fig. 3 and 4, a traffic light control method based on vehicle identification comprises the following steps:
acquiring an image through a camera, preprocessing the image, extracting features of the preprocessed image, and detecting vehicle motion;
counting the passing vehicles between the RFID reader and the traffic intersection through the RFID reader arranged at the front section of the traffic intersection;
combining the motion detection result and the passing vehicle counting result to calculate the length of the pre-passing vehicle queue;
and calculating the queue passing time according to the length of the pre-passing vehicle queue, and controlling the green light lighting time according to the calculated queue passing time.
Preprocessing an image sequentially comprises image graying, image smoothing filtering and image registration; the image smoothing filtering includes any one of mean filtering, weighted average filtering, or median filtering. In this embodiment, median filtering is adopted, so that generally, a video image before filtering has punctate noise, the median filtering has an effect of smoothing the image, and the image edge is kept good.
Image registration is a basic task of image analysis and processing, and is widely applied to the fields of image processing, computer vision, pattern recognition and the like. In real life and scientific research, it is often necessary to image a particular scene with different sensors at different times and under different conditions. In order to comprehensively utilize the obtained picture information and perform deep analysis on the scene, image registration is a basic step. Image registration refers to seeking some spatial transformation for one or more images to make it spatially consistent with a corresponding point on another image. Coincidence here means that the same point on the same imaged object has the same spatial position on both images for which registration has been completed. The essential problem of image registration is to find an image transformation model to correct the deformation of the image.
In the system, the jitter of the camera cannot be ignored, and the jitter can seriously affect the effect of the frame difference method in the subsequent image processing, so that the registration of the images is necessary in the preprocessing. The image registration process includes the following steps: step 1, determining registration characteristic points, and establishing a transformation model between images by using the registration characteristic points. Because the same target is imaged, part of pixel points on the image have a one-to-one mapping relation between the reference image and the floating image, and the pixel points represent the same target. And determining a registration measure function through the registration characteristic points to provide basis for registration optimization. Step 2: and establishing a relation transformation model between the reference image and the floating image, and providing a reference image for image registration. Generally, the coordinate system of one image is taken as the coordinate system of the standard image, and the coordinate system of the other image is taken as the coordinate system of the image to be corrected. And 3, step 3: and searching in a search space by using an optimization function and taking the measure function as a basis to determine a transformation model between the images. And 4, performing spatial transformation on the floating image on the basis of the determination of the transformation model, wherein the spatial transformation and the geometric interpolation are included.
In this embodiment, the feature extraction of the preprocessed image includes: 1. establishing a masking layout with the same size as the image to be processed and the gray values of the same channel number all being 0 (namely black); 2. the method comprises the steps of defining an interested area in an image to be processed, and simultaneously recording coordinates of four vertexes of an irregular quadrangle; 3. taking the four pixel points at the same coordinate of the mask image, and connecting the four pixel points into a closed area; 4. assigning the pixel points in the closed area to be 255 (namely white), and finishing the mask making; 5. and normalizing the mask pixel value, and then performing phase comparison with the image to be processed to extract a characteristic region.
In the system, the images acquired by the cameras at the intersections not only contain lanes to be processed, but also can contain other lanes, even other unnecessary image information such as flower beds, trees, pedestrians, buildings and the like, and if the whole images are processed together, the calculation amount is large, and the statistics of the image information of the relevant lanes is seriously influenced by irrelevant information. Therefore, it is important to set a region of interest in the image to be processed. Taking the middle lane as an example, a vehicle characteristic region is defined, and the rule is as follows:
1. the region is to contain map information of the lane to be processed, other lanes must not be in it;
2. the starting position of the first automobile must contain the first automobile behind the stop line of the crossroad, and the stopping position of the first automobile needs to reach the maximum value agreed by the regulation and control parameters of the traffic light, namely if the vehicles are queued at the position, the queuing length is considered to reach the maximum value, and the extension time of the green light in the direction needs to be the maximum;
3. the region of interest cannot include the white lane line of the lane because the gray gradient change at the edge of the lane line is large, and the gray gradient change can be detected by subsequent edge detection, thereby affecting image statistics.
The vehicle feature of this embodiment need not be shown particularly precisely as long as the above three conditions are satisfied. Because the height of the camera and the shooting angle are fixed when the equipment is erected to the intersection, the shooting angle is not changed under normal conditions (slight deviation caused by vibration is negligible). The shooting range of the image is fixed, and in the special application occasion of a traffic intersection, the stop line of the vehicle is also fixed, so that the position of the stop line of each lane in the image is fixed, and the parameter is used as the head coordinate of the vehicle queue.
In this embodiment, the vehicle motion detection on the image after feature extraction is to perform motion detection on a vehicle by using a frame difference method. To calculate the queue length of the stopped vehicle, the motion of the vehicle needs to be detected first, and the coordinates of the tail of the queue are determined. As shown in fig. 4, in an area with a length L between a video monitoring station at a traffic intersection and an RFID vehicle counting station at the front end of the traffic intersection, if a fleet of vehicles comes to the intersection, the vehicles close to the intersection in front stop at a red light, and the vehicles stop progressively at the very end, so that the coordinates of the vehicles at the end of the fleet are determined to determine the queuing length of the stopped vehicles. The system does not need to extract a complete moving target, and only needs to determine whether the vehicle moves, so that the frame difference method is selected to carry out motion detection on the vehicle
The frame difference method is one of the most commonly used methods for detecting and segmenting the motion landmark, and the basic principle is to perform pixel-based time difference between two continuous frames of images at time K and time K +1 in an image sequence. Firstly, the cue values corresponding to adjacent frames are subtracted to obtain a difference image. Then binarizing the differential image, and if the change of the corresponding image index value is smaller than a predetermined threshold value, considering the image index value as a background pixel; if the image turbulence values of the image areas are greatly changed, the image turbulence values are considered to be caused by moving objects in the image, the areas are marked as foreground pixels, and the positions of the moving month marks in the image can be determined by utilizing the marked pixel areas. Because the time interval between two adjacent frames is short, the previous frame image is used as the background model of the current frame, so that the real-time performance is better, the background does not need to be accumulated, the algorithm is simple, the calculated amount is small, and the updating speed is high.
In this embodiment, a cross-line counting method is used for counting the cross-line vehicles between the RFID reader and the traffic intersection. By taking the position of the RFID reader as a boundary, when a vehicle passes through the RFID reader, the reader identifies and counts once, the last vehicle which just passes through the RFID reader is known to pass, counting is stopped, the counting result is used as the number of saturated vehicles in an area with the length of L shown in figure 4, and then the RFID reader sends the vehicle counting information to a video monitoring station of the intersection through a 4G/5G wireless router as the parameter basis for controlling traffic lights.
Finally, calculating the queue passing time according to the length of the pre-passing vehicle queue, comprising the following steps: the number of vehicles in the road section is set as i +1, the running speed is set as vm/s, the position of the vehicle in the road section is set as xi ═ l ×, wherein u is [0, 1%]The random number is uniformly distributed in the interval, l is the length of the pre-passing vehicle queue, and the estimated time for the vehicle to normally pass through the road section is
This vehicle time is the total time for the vehicle to pass through the area of length L as shown in fig. 4, and therefore the turn-on time of the traffic light green light is controlled to be t.
The present invention is not intended to be limited to the particular embodiments shown and described, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The utility model provides a traffic light control system based on vehicle discernment, is including setting up a plurality of video monitoring website at a plurality of traffic crossroads, the video monitoring website includes camera, controller, traffic light, and camera, controller, traffic light connect gradually, power module, its characterized in that are connected to camera, controller: the video monitoring sites are accessed to a public network through a base station and are in communication connection with an exchanger of a remote monitoring center, the exchanger is connected with a hard disk video recorder and a monitoring client, and the hard disk video recorder is connected with a large monitoring screen; the controller of the video monitoring station is further connected with a first 4G/5G wireless router, the front end of the video monitoring station is further provided with an RFID vehicle counting station, the RFID vehicle counting station comprises an RFID reader, an RFID antenna and a second 4G/5G wireless router, the RFID antenna and the second 4G/5G wireless router are connected with the RFID reader, and the first 4G/5G wireless router and the second 4G/5G wireless router are in wireless communication connection.
2. A traffic light control system based on vehicle identification as claimed in claim 1 wherein: the controller is any one of an ARM processor, a DSP or an FPGA.
3. A traffic light control system based on vehicle identification as claimed in claim 1 wherein: the monitoring client is a computer.
4. A traffic light control system based on vehicle identification as claimed in claim 1 wherein: the power module is a solar panel.
5. A traffic light control method based on vehicle identification is characterized by comprising the following steps:
acquiring an image through a camera, preprocessing the image, extracting features of the preprocessed image, and detecting vehicle motion;
counting the passing vehicles between the RFID reader and the traffic intersection through the RFID reader arranged at the front section of the traffic intersection;
combining the motion detection result and the passing vehicle counting result to calculate the length of the pre-passing vehicle queue;
and calculating the queue passing time according to the length of the pre-passing vehicle queue, and controlling the green light lighting time according to the calculated queue passing time.
6. The traffic light control method based on vehicle identification as claimed in claim 5, wherein: the image preprocessing comprises image graying, image smoothing filtering and image registration in sequence; the image smoothing filtering includes any one of mean filtering, weighted average filtering, or median filtering.
7. The traffic light control method based on vehicle identification as claimed in claim 5, wherein the feature extraction of the preprocessed image comprises:
establishing a masking layout with the same size and the same channel number as the image to be processed and the gray values of all 0;
the method comprises the steps of defining an interested area in an image to be processed, and simultaneously recording coordinates of four vertexes of an irregular quadrangle;
taking the four pixel points at the same coordinate of the mask image, and connecting the four pixel points into a closed area;
assigning the pixel points in the closed area to be 255, and finishing the mask making;
and normalizing the mask pixel value, and then performing phase comparison with the image to be processed to extract a characteristic region.
8. The traffic light control method based on vehicle identification as claimed in claim 5, wherein: and the vehicle motion detection of the image after the characteristic extraction comprises the motion detection of the vehicle by adopting a frame difference method.
9. The traffic light control method based on vehicle identification as claimed in claim 5, wherein: the step of counting the passing vehicles between the RFID reader and the traffic intersection comprises a passing counting method.
10. According toA traffic light control method based on vehicle identification as claimed in claim 5 wherein: the calculating of the queue passing time according to the queue length of the pre-passing vehicles comprises the following steps: the number of vehicles in the road section is set as i +1, the running speed is set as vm/s, the position of the vehicle in the road section is set as xi ═ l ×, wherein u is [0, 1%]The random number is uniformly distributed in the interval, l is the length of the pre-passing vehicle queue, and the estimated time for the vehicle to normally pass through the road section is
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| CN114202938A (en) * | 2021-11-18 | 2022-03-18 | 开研(宁波)信息科技有限公司 | Traffic light control system based on road vehicle number and control method thereof |
| CN114244975A (en) * | 2021-11-15 | 2022-03-25 | 国能大渡河革什扎水电开发有限公司 | Mobile operation process control method and system based on video edge calculation |
| CN114707560A (en) * | 2022-05-19 | 2022-07-05 | 北京闪马智建科技有限公司 | Data signal processing method and device, storage medium and electronic device |
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