CN112419750A - Method for detecting silent low-point outlet channel overflow event - Google Patents
Method for detecting silent low-point outlet channel overflow event Download PDFInfo
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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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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
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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
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- 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
- 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
- G08G1/0133—Traffic data processing for classifying traffic situation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/44—Event detection
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- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
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- G—PHYSICS
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- G06V2201/08—Detecting or categorising vehicles
Abstract
The invention belongs to the field of intelligent traffic, and particularly discloses a silent low-point exit overflow event detection method which is suitable for the condition that the camera erection height is 5-8 m, carries out real-time quantitative classification in a short period on the traffic condition based on four traffic parameters of vehicle number, static-to-slow ratio, space occupancy and target coincidence rate, carries out statistical analysis on the classification result in a time domain, and finally judges whether an exit overflow event occurs to realize automatic detection of a traffic jam event and automatically identify the severity of the jam. The invention can replace manual inspection, realize automatic inspection, find congestion events in time and prompt, so that measures can be taken quickly to solve the problem of traffic congestion.
Description
Technical Field
The invention relates to the field of traffic jam event detection, in particular to a silent low-point exit lane overflow event detection method.
Background
With the rapid development of cities in China, more and more people gather in cities, and the urban population rapidly grows. As the living standard of people is improved, the holding amount of private cars is told to increase, the urban traffic condition is worse and worse, and the traffic jam becomes chronic in urban traffic. Such a situation is more prominent in regional center cities. The events hidden behind the traffic jam events are the traffic jams caused by the reasons, how to solve the traffic jams more quickly and how to take powerful measures to prevent the traffic jams are the problems that need to be paid attention to in the current urban development.
While exit lane overflow events are a special case of traffic congestion events, which are mainly aimed at congestion at intersections (crossroads). As shown in fig. 1 for example, the scenario shown in fig. 1 is a typical intersection scenario, which includes an entrance area, an intersection area, and an exit area, where the entrance area "enters" the intersection area from a one-way road, which is called an "entrance road"; the exit area is "exiting" from the intersection area into a single-row lane, referred to as an "exit lane". The exit lane overflow event is characterized by: because the traffic jam occurs in front of the vehicle, the vehicle cannot normally travel forward after entering the intersection area, and the traffic jam is remained in the intersection area, so that the traffic jam is caused in the intersection area. The exit lane overflow event is a special case of traffic jam, and has a greater influence on traffic conditions, and vehicles in multiple directions cannot normally pass through the exit lane in serious conditions, so that the vehicles are mutually influenced, and large-area jam is caused.
At present, a traffic police part is provided with a specially-assigned person for traffic condition inspection, and the purpose is to find traffic jam events in time. However, the manpower is insufficient, and it is difficult to cope with many road sections. In a regional center city, thousands of intersections are monitored by polling less than 10 people. Only less than 20% of the important road sections can be covered in one day. In reality, the construction of skynet engineering is already perfect, and the key road sections and intersections are monitored by cameras. How to fully utilize video monitoring and utilize an image technology to sense traffic jam events in real time, and timely reporting is a key for solving the problems.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a silent low-point outlet channel overflow event detection method.
In order to achieve the purpose, the invention is implemented according to the following technical scheme:
a silent low point exit lane overflow event detection method is based on a detection and tracking result of a road vehicle target, and appoints an ROI (region of interest) of an intersection scene needing to be detected and tracked in any frame of picture in a video shot by a camera, wherein the camera in the ROI region of the intersection scene needing to be detected and tracked is fixed, and the erection height of the camera is 5-8 m; the detection method comprises the following steps:
s1, dividing an ROI (region of interest) of the intersection scene needing to be detected and tracked into an exit region and an intersection region, and respectively acquiring the number of vehicles, the static-slow ratio, the space occupancy and the target coincidence rate of the vehicles in the exit region and the intersection region; wherein, the number of vehicles: the number of detected vehicles in the exit area and the intersection area; static-slow ratio: in the exit area and the intersection area, the number of vehicles which are static and slow in motion accounts for the percentage of the total number; space occupancy rate: detecting a vehicle object in the exit area and the intersection area, wherein the pixels occupied by the vehicle object frame account for the percentage of the total pixels of the exit area or the intersection area; target overlap ratio: in the exit area and the intersection area, calculating the degree of overlap IOU between the detected vehicle target frames to obtain the average value of the coincidence number of all the vehicle targets, namely the average value of the IOU coincidence targets;
s2, setting a judgment condition of traffic condition quantitative grading of an ROI (region of interest) of an intersection scene needing to be detected and tracked according to parameters of preset vehicle number, static-to-slow ratio, space occupancy and IOU coincidence target mean value, then setting a traffic condition quantitative grading standard according to the judgment condition of the set traffic condition quantitative grading, and quantitatively grading the traffic condition of an outlet region and a cross region of a frame of picture in a video shot by a camera into a congestion state, a middle state and a non-congestion state respectively according to the traffic condition quantitative grading standard; then self-adaptively adjusting preset parameters of the number of vehicles, the static-to-slow ratio, the space occupancy and the IOU coincidence target mean value to serve as parameters for next judgment;
s3, taking continuous multi-frame pictures in a video shot by the camera as a sampling period, and respectively quantizing and grading the traffic conditions of an exit area and a crossing area in the sampling period into a congestion state, a middle state and a non-congestion state according to a traffic condition quantization grading standard;
s4, smoothing the congestion state, the intermediate state and the non-congestion state of the exit area and the intersection area in one sampling period respectively to obtain real-time congestion event information of the exit area and the intersection area, wherein the real-time congestion event information of the exit area and the intersection area comprises the congestion state and the non-congestion state;
s5, when the congestion event information of the exit area and the intersection area is congestion, judging that an exit channel overflow event occurs, and judging that other exit channel overflow events are non-exit channel overflow events;
s6, on the basis of S5, a plurality of sampling periods form a long time domain, in the long time domain, the severity of an outlet channel overflow event is measured by calculating the proportion of the time of the outlet channel overflow event to the total time, namely the duty ratio of the outlet channel overflow event, and the hopping frequency between the outlet channel overflow event and a non-outlet channel overflow event, wherein the higher the duty ratio of the outlet channel overflow event is, the lower the hopping frequency is, and the more serious the congestion is.
In the calculation of the space occupation ratio in S1, when the distance between the vehicles in the exit area or the intersection area is relatively short and the gap cannot accommodate one vehicle, the pixels actually occupied by the vehicles are circumscribed polygonal frames of the city surrounded by the two vehicle target frames.
As a preferable embodiment of the present invention, the S2 specifically includes:
classifying road traffic scenes into a large scene and a small scene according to the size of an exit area and a cross area and the maximum number of vehicles which can be accommodated; large scene: when the space occupancy reaches 80%, the number of accommodated vehicles reaches M and scenes above M; small scene: when the space occupancy reaches 80%, accommodating scenes with the number of vehicles being less than N; wherein M > N, M, N are integers greater than zero;
the judgment conditions of the traffic condition quantitative grading are as follows:
the first condition is as follows: number of vehicles > number of vehicles preset;
and a second condition: the static-to-slow ratio is greater than a preset static-to-slow ratio;
and (3) carrying out a third condition: the space occupancy is greater than a preset space occupancy;
and a fourth condition: the IOU coincidence target mean value is larger than a preset IOU coincidence target mean value;
the traffic condition quantitative grading standard comprises a large scene quantitative grading standard and a small scene quantitative grading standard, wherein the large scene quantitative grading standard is as follows:
congestion state: the four conditions are all satisfied;
an intermediate state: the above conditions are more than any two, and any one of the first condition and the third condition must be satisfied;
non-congestion state: a condition that does not satisfy the congestion status and the intermediate status;
the small scene quantization grading standard is as follows:
congestion state: the second condition is met, and the third condition and the fourth condition are both met;
an intermediate state: the second condition, the third condition and the fourth condition satisfy any one of the conditions;
non-congestion state: the conditions of the congestion state and the intermediate state are not satisfied.
The preset parameters for adaptively adjusting the number of vehicles, the static-to-slow ratio, the space occupancy and the IOU coincidence target mean value are as follows:
after a congestion event is captured, the maximum values of the number of vehicles, the static-to-moderate ratio, the space occupancy and the IOU coincidence target mean value which are depended on for judging congestion are calculated, new parameters are calculated according to a formula (1) to serve as current configuration parameters, and the most suitable adjustment of the parameters is achieved:
new＝max*scale (1)
in the formula (1), new represents a new parameter, max is a maximum value of the parameter during congestion, scale is a proportion of reduction of the maximum value which can be set, and the scale value is greater than 0 and less than 1.
As a preferable aspect of the present invention, before the traffic condition quantization step in one sampling period in S3, the method further includes:
and respectively counting parameters of the number of vehicles, the static-slow ratio, the space occupancy and the IOU coincidence target mean value of an exit area and a cross area in a sampling period, deleting the maximum m samples and the minimum N samples of a sample set obtained by calculating any parameter of the number of vehicles, the static-slow ratio, the space occupancy and the IOU coincidence target mean value in one sampling period to obtain the remaining K samples, then calculating the mean value of the K samples, only keeping the samples which are more than the mean value, and calculating the mean value as a final parameter value.
As a preferred embodiment of the present invention, in S6, the method further includes defining the outlet channel overflow event as "1" high level, and defining the non-outlet channel overflow event as "0" low level, and obtaining a rectangular wave jumping between 1 and 0 high and low levels, where the higher the duty ratio of the high level, the more serious the congestion.
As a preferable aspect of the present invention, the duration of the sampling period in S3 is 1 second.
Compared with the prior art, the invention has the following beneficial effects:
the invention is suitable for the condition that the erection height of the camera is 5-8 m, and based on four traffic parameters of vehicle number, static-to-slow ratio, space occupancy and target coincidence rate, the traffic conditions of an outlet area and a crossing area are quantitatively graded in a real-time short period, the grading result is statistically analyzed in a time domain, and whether an outlet channel overflow event occurs is finally judged; the automatic detection of the overflow event of the outlet channel can be realized, the congestion severity can be automatically identified, the manual inspection can be replaced, the automatic inspection and the alarm can be realized, the congestion event can be timely found, the prompt is given, and the traffic congestion problem can be quickly solved by taking measures.
Drawings
FIG. 1 is a diagram illustrating a conventional exit channel overflow event.
FIG. 2 is a schematic diagram of an ROI area of an agreed intersection scene according to the present invention.
FIG. 3 is a flow chart of a silent low point exit lane overflow event detection method of the present invention.
FIG. 4 is a schematic diagram of IOU calculation according to an embodiment of the present invention.
FIG. 5 is a schematic diagram illustrating space occupancy calculation according to an embodiment of the present invention.
FIG. 6 is a flow chart illustrating calculation of parameters during a sampling period according to an embodiment of the present invention.
FIG. 7 is a diagram illustrating an example of sampling period exit lane overflow events and logic determination.
FIG. 8 is a schematic diagram illustrating congestion analysis for an exit channel overflow event in a long-term space, according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. The specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Fig. 2 and fig. 3 show an embodiment of the present invention, which specifically discloses a silent low point exit lane overflow event detection method, based on the detection and tracking result of a road vehicle target, it should be noted that, in this embodiment, the detection and tracking result of the road vehicle target may be detected according to the existing vehicle target detection and tracking technology, in order to obtain the ROI area of the intersection scene that needs to be detected and tracked, then appointing an ROI (region of interest) region of an intersection scene needing to be detected and tracked of any frame of picture in a video shot by a camera, wherein the ROI region of the intersection scene is represented by any polygon, the ROI region generation of the intersection scene can be automatically generated by manual marking and algorithm, and the technical means adopted for detecting and tracking and automatically generating a road region are not in the scope of the invention and are not repeated herein; the camera in the ROI area of the intersection scene needing to be detected and tracked is fixed, and the erection height of the camera is 5-8 m; the method for detecting the silent low-point outlet channel overflow event comprises the following specific steps:
dividing an ROI (region of interest) region of an intersection scene needing to be detected and tracked into an exit region and an intersection region, and respectively acquiring 4 traffic parameters in the exit region and the intersection region:
number of vehicles: the number of detected vehicles in the exit area and the intersection area;
static-slow ratio: the number of vehicles which are static and slow in motion is a percentage of the total number in the exit area and the intersection area;
space occupancy rate: detecting a vehicle object in the exit area and the intersection area, wherein the pixels occupied by the vehicle object frame account for the percentage of the total pixels of the exit area or the intersection area; in practical tests, it is found that if only the rectangular area occupied by the vehicle is calculated as the vehicle-occupied pixels, the calculated space occupancy is lower than the true occupancy. In reality there is a gap between the vehicles which is not sufficient to pass a vehicle, and it should be assumed that the vehicle is not occupied. Based on the phenomenon, the invention designs a calculation method of space occupancy in a targeted manner; when the distance between the vehicles is relatively close and the gap cannot accommodate one vehicle, the pixels actually occupied by the vehicle are circumscribed polygons defined by two vehicle target rectangular frames, and the polygons are semi-transparent hexagons enclosed by dotted lines as shown in fig. 5.
Target overlap ratio: in the exit area and the intersection area, the average value of the coincidence numbers of all the vehicle targets, that is, the average value of the IOU coincidence targets, is obtained by performing IOU calculation on the detected vehicle target frames, and the IOU calculation method is shown in FIG. 4, and belongs to the prior art, and the details are not repeated in this embodiment.
In reality, scenes are numerous, and in order to better achieve sufficient robustness for more scenes, the scenes are classified into two types of large scenes and small scenes according to the sizes of an exit region and an intersection region and the maximum number of vehicles which can be accommodated.
Large scene: when the space occupancy reaches 80%, the number of the accommodated vehicles reaches M (which can be configured according to actual requirements) and scenes above M;
small scene: when the space occupancy reaches 80%, the number of the accommodated vehicles is N (the vehicles can be configured according to actual requirements) and the number of the accommodated vehicles is less than N; where M > N, M, N are integers greater than zero.
Traffic condition quantitative grading standard formulated by the invention "
In the present invention, the following conditions are taken as basic judgment conditions (all of the following conditions are preset parameters, and the present invention designs a specific adaptive adjustment scheme for these parameters):
the first condition is as follows: number of vehicles >10 (adaptively configurable);
and a second condition: a percentage of stationary vehicles in total > 50% (adaptively configurable);
and (3) carrying out a third condition: space occupancy > 60% (adaptively configurable);
and a fourth condition: iou coincidence target mean value is greater than 2;
in the invention, the large scene and the small scene are quantized and graded according to the following quantization standard
Large scene quantitative grading standard:
congestion (level one): the four conditions are all satisfied, and the congestion state is achieved;
intermediate state (secondary): the above conditions are more than any two, and any one of the first condition and the third condition must be satisfied;
no congestion (third level): the case of not meeting the primary and secondary criteria is three.
Small scene quantitative grading standard:
congestion (level one): meanwhile, the congestion state is achieved when the second condition is met, and the third condition and the fourth condition are both met;
intermediate state (secondary): the second condition, the third condition and the fourth condition satisfy any one of the conditions;
no congestion (third level): the case of not meeting the primary and secondary criteria is three.
The self-adaptive adjustment scheme of the congestion judgment parameter comprises the following steps:
after the congestion event is captured, 4 condition parameters depended on for judging congestion are counted to obtain respective maximum values, and the parameter values under the current scene are self-adapted according to a calculation formula adopting a formula 1 and serve as parameters for judging next time.
new max scale (sacle <1 configurable parameter) (1)
The new parameter is shown in the above, max is the maximum value of the parameter during congestion, and scale is the rate of reduction of the maximum value. For 4 traffic parameters, a default parameter is configured at system initialization, and the default parameter is set to a smaller value, which is expected to be more sensitive to the perception of congestion events. However, the smaller the default value, the easier it is to falsely detect, and the default parameters are difficult to adapt to all traffic scenarios. Therefore, after the congestion event is captured, the maximum value of the 4 traffic parameters is obtained, and new parameters are obtained according to the formula 1 to serve as the current configuration parameters, so that the parameters are adaptively adjusted. The scale value must be less than 1 and it is desirable to detect traffic congestion events before the maximum value of each parameter has been reached. The higher the scale value is, the more strict the requirement for the congestion event is, and the more likely the missed detection is caused, but the higher the confidence of the detected congestion event is. The smaller the scale value, the lower the demand for congestion events, the higher the sensitivity, and the less likely to miss detection. But are more prone to false detection.
The grading criteria are quantified based on congestion status, but not every frame of image in real time. And the traffic state of the exit area and the intersection area is further quantized and graded (namely weak real-time detection) according to the following standard, and the quantized grading method formulated by the invention is as follows:
and taking continuous multi-frame pictures in a video shot by a camera as a sampling period, and respectively quantizing and grading the traffic conditions of an outlet area and a cross area in the sampling period into a congestion state, a middle state and a non-congestion state according to a traffic condition quantization grading standard.
The continuous N frames are used as a sampling period, the 4 traffic parameters in the sampling period are counted, the values of the 4 traffic parameters in the sampling period are calculated according to the flow of the figure 6, and the traffic conditions of an outlet area and a cross area in the sampling period are respectively quantized and graded into a congestion state, a middle state and a non-congestion state according to a traffic condition quantization grading standard, wherein the sampling period is 1 second. Due to the existence of the target false detection and missing detection problems, 4 parameters obtained by utilizing detection tracking calculation can be larger or smaller than the true value. In order to reduce the influence of false detection and missed detection, as shown in fig. 6, for N samples of a sample set obtained by calculating an arbitrary parameter in one sampling period, the maximum m samples and the minimum N samples are deleted first, and the remaining K samples are obtained. And then averaging K samples, only keeping the samples larger than the average value, and averaging the samples to serve as final parameter values. The reason why the average value of the samples larger than the average value is used as the final parameter is that target omission occurs more easily in practice, and the probability of each parameter is higher than the true value, so that the average value of each sample larger than the average value is used as the final parameter.
As shown in fig. 7, smoothing is performed on congestion states, intermediate states and non-congestion states of an exit area and an intersection area in one sampling period to obtain congestion event information of the exit area and the intersection area in real time, where the congestion event information of the exit area and the intersection area in real time includes the congestion state and the non-congestion state; and when the congestion event information of the exit area and the intersection area is congestion at the same time, judging that an exit channel overflow event occurs, and judging that other exit channel overflow events are non-exit channel overflow events. By the above-described method, real-time congestion information is obtained with the sampling interval as the minimum unit.
According to the business requirements, people are more concerned about congestion events with long congestion time, and the congestion is more serious the longer the congestion time is, the more serious the congestion is. The method comprises the steps that a plurality of sampling periods form a long time domain, in the long time domain, the severity of an outlet channel overflow event is measured by calculating the proportion of the time of the outlet channel overflow event to the total time, namely the duty ratio of the outlet channel overflow event, and the hopping frequency between the outlet channel overflow event and a non-outlet channel overflow event, wherein the higher the duty ratio of the outlet channel overflow event is, the lower the hopping frequency is, and the more serious the congestion is. As shown in fig. 8, congestion is defined as "1" high level, and non-congestion is defined as "0" low level, and a rectangular wave that jumps between 1 and 0 at high and low levels can be obtained, where the higher the duty ratio of the high level, the more serious the congestion is.
The technical solution of the present invention is not limited to the limitations of the above specific embodiments, and all technical modifications made according to the technical solution of the present invention fall within the protection scope of the present invention.
Claims (6)
1. A silent low point exit lane overflow event detection method is based on a detection and tracking result of a road vehicle target, and appoints an ROI (region of interest) of an intersection scene needing to be detected and tracked in any frame of picture in a video shot by a camera, wherein the camera in the ROI region of the intersection scene needing to be detected and tracked is fixed, and the erection height of the camera is 5-8 m; the detection method is characterized by comprising the following steps:
s1, dividing an ROI (region of interest) of the intersection scene needing to be detected and tracked into an exit region and an intersection region, and respectively acquiring the number of vehicles, the static-slow ratio, the space occupancy and the target coincidence rate of the vehicles in the exit region and the intersection region; wherein, the number of vehicles: the number of detected vehicles in the exit area and the intersection area; static-slow ratio: in the exit area and the intersection area, the number of vehicles which are static and slow in motion accounts for the percentage of the total number; space occupancy rate: detecting a vehicle object in the exit area and the intersection area, wherein the pixels occupied by the vehicle object frame account for the percentage of the total pixels of the exit area or the intersection area; target overlap ratio: in the exit area and the intersection area, calculating the degree of overlap IOU between the detected vehicle target frames to obtain the average value of the coincidence number of all the vehicle targets, namely the average value of the IOU coincidence targets;
s2, setting a judgment condition of traffic condition quantitative grading of an ROI (region of interest) of an intersection scene needing to be detected and tracked according to parameters of preset vehicle number, static-to-slow ratio, space occupancy and IOU coincidence target mean value, then setting a traffic condition quantitative grading standard according to the judgment condition of the set traffic condition quantitative grading, and quantitatively grading the traffic condition of an outlet region and a cross region of a frame of picture in a video shot by a camera into a congestion state, a middle state and a non-congestion state respectively according to the traffic condition quantitative grading standard; then self-adaptively adjusting preset parameters of the number of vehicles, the static-to-slow ratio, the space occupancy and the IOU coincidence target mean value to serve as parameters for next judgment;
s3, taking continuous multi-frame pictures in a video shot by the camera as a sampling period, and respectively quantizing and grading the traffic conditions of an exit area and a crossing area in the sampling period into a congestion state, a middle state and a non-congestion state according to a traffic condition quantization grading standard;
s4, smoothing the congestion state, the intermediate state and the non-congestion state of the exit area and the intersection area in one sampling period respectively to obtain real-time congestion event information of the exit area and the intersection area, wherein the real-time congestion event information of the exit area and the intersection area comprises the congestion state and the non-congestion state;
s5, when the congestion event information of the exit area and the intersection area is congestion, judging that an exit channel overflow event occurs, and judging that other exit channel overflow events are non-exit channel overflow events;
s6, on the basis of S5, a plurality of sampling periods form a long time domain, in the long time domain, the severity of an outlet channel overflow event is measured by calculating the proportion of the time of the outlet channel overflow event to the total time, namely the duty ratio of the outlet channel overflow event, and the hopping frequency between the outlet channel overflow event and a non-outlet channel overflow event, wherein the higher the duty ratio of the outlet channel overflow event is, the lower the hopping frequency is, and the more serious the congestion is.
2. The method for detecting the overflow event of the silent low spot exit lane as claimed in claim 1, wherein in the step S1, when the distance between the vehicles in the exit area or the intersection area is relatively close and the gap cannot accommodate one vehicle, the pixels actually occupied by the vehicles are the circumscribed polygon frame of the city enclosed by the two vehicle target frames.
3. The method according to claim 1, wherein the S2 specifically includes:
classifying road traffic scenes into a large scene and a small scene according to the size of an exit area and a cross area and the maximum number of vehicles which can be accommodated; large scene: when the space occupancy reaches 80%, the number of accommodated vehicles reaches M and scenes above M; small scene: when the space occupancy reaches 80%, accommodating scenes with the number of vehicles being less than N; wherein M > N, M, N are integers greater than zero;
the judgment conditions of the traffic condition quantitative grading are as follows:
the first condition is as follows: number of vehicles > number of vehicles preset;
and a second condition: the static-to-slow ratio is greater than a preset static-to-slow ratio;
and (3) carrying out a third condition: the space occupancy is greater than a preset space occupancy;
and a fourth condition: the IOU coincidence target mean value is larger than a preset IOU coincidence target mean value;
the traffic condition quantitative grading standard comprises a large scene quantitative grading standard and a small scene quantitative grading standard, wherein the large scene quantitative grading standard is as follows:
congestion state: the four conditions are all satisfied;
an intermediate state: the above conditions are more than any two, and any one of the first condition and the third condition must be satisfied;
non-congestion state: a condition that does not satisfy the congestion status and the intermediate status;
the small scene quantization grading standard is as follows:
congestion state: the second condition is met, and the third condition and the fourth condition are both met;
an intermediate state: the second condition, the third condition and the fourth condition satisfy any one of the conditions;
non-congestion state: the conditions of the congestion state and the intermediate state are not satisfied.
The preset parameters for adaptively adjusting the number of vehicles, the static-to-slow ratio, the space occupancy and the IOU coincidence target mean value are as follows:
after a congestion event is captured, the maximum values of the number of vehicles, the static-to-moderate ratio, the space occupancy and the IOU coincidence target mean value which are depended on for judging congestion are calculated, new parameters are calculated according to a formula (1) to serve as current configuration parameters, and the most suitable adjustment of the parameters is achieved:
new＝max*scale (1)
in the formula (1), new represents a new parameter, max is a maximum value of the parameter during congestion, scale is a proportion of reduction of the maximum value which can be set, and the scale value is greater than 0 and less than 1.
4. The method of claim 3, wherein the step of S3, before the quantitative classification of traffic conditions in a sampling period, further comprises:
and respectively counting parameters of the number of vehicles, the static-slow ratio, the space occupancy and the IOU coincidence target mean value of an exit area and a cross area in a sampling period, deleting the maximum m samples and the minimum N samples of a sample set obtained by calculating any parameter of the number of vehicles, the static-slow ratio, the space occupancy and the IOU coincidence target mean value in one sampling period to obtain the remaining K samples, then calculating the mean value of the K samples, only keeping the samples which are more than the mean value, and calculating the mean value as a final parameter value.
5. The silent low point exit channel overflow event detection method as claimed in claim 1, wherein in said S6, further comprising defining an exit channel overflow event as "1" high level and a non-exit channel overflow event as "0" low level, obtaining a rectangular wave with jump between 1 and 0, wherein the higher the duty cycle of the high level, the more serious the congestion.
6. The silent low point exit lane overflow event detection method of claim 1, wherein the duration of the sampling period in S3 is 1 second.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202010954214.8A CN112419750B (en) | 2020-09-11 | 2020-09-11 | Method for detecting silent low-point outlet channel overflow event |
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