CN107248290A - The hybrid mainline toll station traffic conflict evaluation method recognized based on automobile video frequency - Google Patents
The hybrid mainline toll station traffic conflict evaluation method recognized based on automobile video frequency Download PDFInfo
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- CN107248290A CN107248290A CN201710573312.5A CN201710573312A CN107248290A CN 107248290 A CN107248290 A CN 107248290A CN 201710573312 A CN201710573312 A CN 201710573312A CN 107248290 A CN107248290 A CN 107248290A
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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
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
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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
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
- G06V20/42—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items of sport video content
Abstract
The invention discloses a kind of hybrid mainline toll station traffic conflict evaluation method recognized based on automobile video frequency, comprise the following steps：1st, camera, traveling video of the collection vehicle in toll plaza are arranged in the toll plaza of charge station；2nd, vehicle identification and tracking are carried out to the vehicle traveling video collected, obtains the position coordinates of vehicle in each two field picture；3rd, using the M frame image datas of vehicle, speed of the vehicle in each two field picture is calculatedWith deceleration indexIn the event of with conflict of speeding, the time index TTC of vehicle following-model is calculatedk；In the event of lane change conflict, Vehicular turn model time index T is calculatedpet；5th, willTTCkAnd TpetPeerization processing is carried out, unified is traffic conflict evaluation index STC, and the order of severity of traffic conflict is judged according to STC value.This method can quantify to the order of severity of charge station's traffic conflict, have important application value to decision-makings such as charge station's Road Safety Evaluation, managed operation, road drainage measures.
Description
Technical field
The invention belongs to freeway management technical field, and in particular to a kind of charge station's traffic conflict evaluation method.
Background technology
With developing rapidly for China in Recent Years economy, the mileage of highway is also constantly increasing.And charge station's conduct
" throat " area of highway, it decides the traffic capacity and service level in the even whole section in section near charge station.
With the increase year by year of vehicle flowrate, the traffic loading that mainline toll station undertakes is also increasing.Charge station of China is not mostly using
The mode that parking automatic charging is combined with parking semi automatic toll.Although the congestion for alleviating charge station to a certain extent is asked
Topic, but this hidden danger that may also cause the accident.
With the development of traffic analysis technology, the traffic conflict technique theory at this stage be mainly used in urban road and
Intersection, the analysis to charge station's traffic conflict is less, and utilizes Video processing and the traffic conflict of analytical technology analysis charge station
The research of problem is even more blank out.
The content of the invention
Goal of the invention：For problems of the prior art, recognized the invention provides a kind of based on automobile video frequency
Hybrid mainline toll station traffic conflict evaluation method, this method can be measured to the order of severity of charge station's traffic conflict
Change, have important application value to decision-makings such as charge station's Road Safety Evaluation, managed operation, road drainage measures.
Technical scheme：The present invention is adopted the following technical scheme that：Handed over based on the hybrid mainline toll station that automobile video frequency is recognized
Logical conflict evaluation method, comprises the following steps：
(1) camera is arranged in the toll plaza of charge station, the traveling using camera collection vehicle in toll plaza is regarded
Frequently；
(2) vehicle identification and tracking are carried out to the vehicle traveling video collected, obtains the position of vehicle in each two field picture
Put coordinate；
(3) using the M frame image datas of vehicle, speed of the vehicle p in the i-th frame is calculated
WhereinFor vehicle p the i-th frame position coordinates,Sat for vehicle p in the position of the i-th-M frames
Mark, △ t are the acquisition time interval of M two field pictures, i>M；
Calculate deceleration indexs of the vehicle p in the i-th frame to i-M frames
(4) if vehicle p and vehicle q are clashed in kth frame, calculating refers in the time of the vehicle following-model of kth frame
Mark TTCk：
WhereinFor vehicle q kth frame position coordinates, vehicle q be in kth frame image vehicle p with vehicle of speeding；
Calculate the Vehicular turn model time index T in kth framepet：
Tpet=Fpe-Fdr
Wherein FdrPicture frame sequence number when being reduced speed now for front vehicle；FpeIt is the picture frame sequence number that vehicle is clashed；
(5) willTTCkAnd TpetPeerization processing is carried out, unified is traffic conflict evaluation index STC, according to STC's
Value judges the order of severity of traffic conflict.
It is in the camera quantity N of toll plaza arrangement in step (1)：
Wherein m is toll collection lanes number, and I is the toll collection lanes number that camera can be photographed；For the fortune that rounds up
Calculate.
Camera position is：Camera is just set to charge station widening side road, will be imaged in non-side of widening
Head is just set to road.
Vehicle identification is carried out to the video collected in step (2) and tracking is specifically included：
(2.1) preliminary prospect is generated to the video image of shooting, binaryzation, dilation erosion processing is carried out, before generation detection
Scape；
(2.2) modeling and frame difference method are eliminated with BSM backgrounds, tracking is identified to the moving vehicle in video；
(2.3) position coordinates and picture frame sequence number of the detection vehicle in each frame are extracted.
The change in location of vehicle is very small in successive frame, is counted in step 3 using 6 frame image datas of automobile video frequency
Calculate, i.e. M=6.
Following equation is pressed in step (5) willTTCkAnd TpetPeerization processing is carried out, unified is traffic conflict evaluation
Index S TC：
Wherein α, beta, gamma, μ is conversion coefficient, is constant.
, will in order to prevent the error of single index from being impacted to judged resultTTCkAnd TpetAfter at the same levelization processing
Traffic conflict evaluation index STCTTC、STCDRAnd STCTpetBe converted to comprehensive traffic conflict evaluation index R：
Wherein λ is weight coefficient；The order of severity of traffic conflict is judged according to R values.
Work as R>When 1.15, it is determined as serious traffic conflict；When 0.9<R<When 1.15, it is determined as moderate traffic conflict；When 0.7
<R<When 0.9, it is determined as slight traffic conflict.
Beneficial effect：Compared with prior art, the hybrid main line charge disclosed by the invention recognized based on automobile video frequency
Traffic conflict evaluation method of standing has advantages below：1st, this method is investigated without manual site, it is easy to obtain data, forms network
Camera can obtain multi-faceted video data, amount of storage is big, can call at any time, calculates quick accurate；2nd, the present invention is set up
Charge station's traffic conflict evaluation model, calculate simple, considered time class, speed class index to the serious journey of traffic conflict
The contribution degree of degree, has important applying value to charge station's operation management, road drainage measure decision-making.
Brief description of the drawings
Fig. 1 is the hybrid mainline toll station traffic conflict characteristic research method flow diagram being fitted based on video；
Fig. 2 is that charge station's camera arrangement schematic diagram is widened in unilateral transition；
Fig. 3 is the video detection foreground picture after the video foreground figure of generation and processing；
Fig. 4 is moving vehicle recognition and tracking figure.
Embodiment
With reference to the accompanying drawings and detailed description, the present invention is furture elucidated.
The present embodiment is to gather the hybrid mainline toll station morning 8 on Shanghai-Hangzhou-Ningbo S2 highways:30-8:34 shootings
The video of totally 3 points of 50 seconds durations, is analyzed charge station's traffic conflict problem.
The invention provides a kind of hybrid mainline toll station traffic conflict evaluation method recognized based on automobile video frequency,
As shown in figure 1, comprising the following steps：
Step 1, charge station toll plaza arrange camera, using camera collection vehicle toll plaza traveling
Video；
In toll plaza, the camera quantity N of arrangement is：
Wherein m is toll collection lanes number, and I is the toll collection lanes number that camera can be photographed, and is had with camera parameter
Close, generally 4-6, camera used can photograph 6 toll collection lanes in the present embodiment；For the computing that rounds up.This
Charge station's one direction has 10 toll collection lanes, it is necessary to arrange in embodiment
(widened substantially without transition, unilateral transition is widened, bilateral according to the 3 of charge station toll plaza kinds of road widening forms
Transition is widened, in order to avoid road widening make it is unsighted cause vehicle identification incomplete, widening side road by camera just
Charge station is arranged, just set camera to road in non-side of widening.Charge station's unilateral direction toll plaza form is
Form is widened in one side, and just camera is arranged to charge station at widening road.Fig. 2 is two cameras of charge station in the present embodiment
Schematic diagram is arranged, wherein camera 1 is just placed to charge station, and camera 2 is just placed to road.
Step 2, vehicle identification and tracking are carried out to the vehicle traveling video collected, obtain vehicle in each two field picture
Position coordinates, specifically includes following steps：
(2.1) preliminary prospect is generated to the video image of shooting, binaryzation, dilation erosion processing is carried out, before generation detection
Scape, such as Fig. 3 (a) show preliminary prospect schematic diagram, and Fig. 3 (b) is to pass through pretreated detection prospect schematic diagram；
(2.2) modeling and frame difference method are eliminated with BSM backgrounds, tracking, such as Fig. 4 is identified to the moving vehicle in video
It is shown, the driving vehicle in square frame to identify；
(2.3) position coordinates and picture frame sequence number of the detection vehicle in each frame are extracted, as shown in table 1, for the 3 of acquisition
The position coordinates and the picture frame sequence number in gathered video of car.
Table 1
Step 3, the M frame image datas using vehicle, calculate speed of the vehicle p in the i-th frame
WhereinFor vehicle p the i-th frame position coordinates,Sat for vehicle p in the position of the i-th-M frames
Mark, △ t are the acquisition time interval of M two field pictures, i>M；
Calculate deceleration indexs of the vehicle p in the i-th frame to i-M frames
The change in location of vehicle is very small in successive frame, and M value is too small, and the position of vehicle is almost unchanged, calculates
Velocity accuracy is not high, if value is too big, and vehicle location change is too many, and time interval is also big, possibly can not reflect vehicle
The details of change in location, also influences whether computational accuracy.Use frequency for 24Hz camera in the present embodiment, regarded using vehicle
6 frame image datas of frequency are calculated, i.e. M=6, and the △ t in such above-mentioned formula are 0.25 second.
(4) if vehicle p and vehicle q are clashed in kth frame, calculating refers in the time of the vehicle following-model of kth frame
Mark TTCk：
WhereinFor vehicle q kth frame position coordinates, vehicle q be in kth frame image vehicle p with vehicle of speeding；
Calculate the Vehicular turn model time index T in kth framepet：
Tpet=Fpe-Fdr
Wherein FdrPicture frame sequence number when being reduced speed now for front vehicle；FpeIt is the picture frame sequence number that vehicle is clashed；
Traffic conflict analysis indexes, the result of calculation such as institute of table 2 are calculated to many traffic conflicts in video and the vehicle being related to
Show, wherein DR is front vehicle deceleration index when occurring traffic conflict.
Table 2
| With the sequence number that conflicts of speeding | Conflict car number | TTC(s) | DR(m/s2) |
| 1 | 32-33 | 4.09 | 1.36 |
| 2 | 66-73 | 2.81 | 0.32 |
| 3 | 73-79 | 4.82 | 0.44 |
| 4 | 79-80 | 7.11 | 1.67 |
| Lane change conflict sequence number | Conflict car number | Tpet(s) | DR(m/s2) |
| 1 | 1-4 | 1.76 | 0.64 |
| 2 | 66-70 | 2.13 | — |
| 3 | 101-102 | 0.75 | — |
Step 5, generalTTCkAnd TpetPeerization processing is carried out, unified is traffic conflict evaluation index STC, according to
STC value judges the order of severity of traffic conflict.
Will by following equationTTCkAnd TpetPeerization processing is carried out, unified is traffic conflict evaluation index STC：
Wherein α, beta, gamma, μ is conversion coefficient, is constant.In the present embodiment, α=2, β=0.491, γ=2, μ=
0.36, concrete outcome is as shown in table 3.
Table 3
| TTC | 0-3s | 3-5s | 5-8s | >8s |
| STCTTC | >1.15 | 0.9-1.15 | 0.7-0.9 | 0-0.7 |
| The order of severity | Seriously | Moderate | Slightly | Nothing |
| Tpet | 0-1.5s | 1.5-2.5s | 2.5-4s | >4s |
| STCTpet | >1.15 | 0.9 | 0.7-0.9 | 0-0.7 |
| The order of severity | Seriously | Moderate | Slightly | Nothing |
| DR | >5m/s2 | 3-5m/s2 | 2-3m/s2 | <2m/s2 |
| STCDR | >1.15 | 0.9 | 0.7-0.9 | <0.7 |
| The order of severity | Seriously | Moderate | Slightly | Nothing |
According to STCTTC、STCDRAnd STCTpetTraffic conflict degree can be judged, in order to prevent the mistake of single index
Difference is impacted to judged result, willTTCkAnd TpetTraffic conflict evaluation index STC after at the same levelization processingTTC、STCDR
And STCTpetBe converted to comprehensive traffic conflict evaluation index R to quantify traffic conflict degree, judge that traffic is rushed according to R values
The prominent order of severity, R's is calculated as follows：
Wherein λ is weight coefficient, and span is 0.1~0.9, and concrete numerical value can be according to road conditions, vehicle, charge station place
Determined Deng objective information, λ values are bigger when general saturation degree is lower；λ values are smaller when saturation degree is higher；The present embodiment period receives
Take station saturation degree smaller, it is 0.75 to take λ, and the final result for calculating and judging is as shown in table 4.Work as R>When 1.15, it is determined as serious
Traffic conflict；When 0.9<R<When 1.15, it is determined as moderate traffic conflict；When 0.7<R<When 0.9, it is determined as slight traffic conflict.
Table 4
| Traffic conflict sequence number | Conflict car number | R | The order of severity |
| 1 | 1-4 | 1.07 | Moderate |
| 2 | 32-33 | 0.99 | Moderate |
| 3 | 66-70 | 0.97 | Moderate |
| 4 | 66-73 | 1.19 | Seriously |
| 5 | 73-79 | 0.91 | Moderate |
| 6 | 79-80 | 0.75 | Slightly |
| 7 | 101-102 | 1.63 | Seriously |
Claims (8)
1. the hybrid mainline toll station traffic conflict evaluation method recognized based on automobile video frequency, it is characterised in that including as follows
Step：
(1) charge station toll plaza arrange camera, using camera collection vehicle toll plaza traveling video；
(2) vehicle identification and tracking are carried out to the vehicle traveling video collected, the position for obtaining vehicle in each two field picture is sat
Mark；
(3) using the M frame image datas of vehicle, speed of the vehicle p in the i-th frame is calculated
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Wherein FdrPicture frame sequence number when being reduced speed now for front vehicle；FpeIt is the picture frame sequence number that vehicle is clashed；
(5) willTTCkAnd TpetPeerization processing is carried out, unified is traffic conflict evaluation index STC, is sentenced according to STC value
The order of severity of disconnected traffic conflict.
2. the hybrid mainline toll station traffic conflict evaluation method according to claim 1 recognized based on automobile video frequency,
Characterized in that, the camera quantity N arranged in step (1) in toll plaza is：
Wherein m is toll collection lanes number, and I is the toll collection lanes number that camera can be photographed；For the computing that rounds up.
3. the hybrid mainline toll station traffic conflict evaluation method according to claim 1 recognized based on automobile video frequency,
Characterized in that, the camera position that toll plaza is arranged in step (1) is：Side road is being widened by camera just to charge
Stand setting, just set camera to road in non-side of widening.
4. the hybrid mainline toll station traffic conflict evaluation method according to claim 1 recognized based on automobile video frequency,
Characterized in that, step (2) is specifically included：
(2.1) preliminary prospect is generated to the video image of shooting, carries out binaryzation, dilation erosion processing, generate detection prospect；
(2.2) modeling and frame difference method are eliminated with BSM backgrounds, tracking is identified to the moving vehicle in video；
(2.3) position coordinates and picture frame sequence number of the detection vehicle in each frame are extracted.
5. the hybrid mainline toll station traffic conflict evaluation method according to claim 1 recognized based on automobile video frequency,
Characterized in that, being calculated in step 3 using 6 frame image datas of automobile video frequency, i.e. M=6.
6. the hybrid mainline toll station traffic conflict evaluation method according to claim 1 recognized based on automobile video frequency,
Will characterized in that, pressing following equation in step (5)TTCkAnd TpetPeerization processing is carried out, it is unified to be commented for traffic conflict
Valency index S TC：
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7. the hybrid mainline toll station traffic conflict evaluation method according to claim 6 recognized based on automobile video frequency,
Characterized in that, willTTCkAnd TpetTraffic conflict evaluation index STC after at the same levelization processingTTC、STCDRAnd STCTpetTurn
It is changed to comprehensive traffic conflict evaluation index R：
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<mi>STC</mi>
<mrow>
<mi>D</mi>
<mi>R</mi>
</mrow>
</msub>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>STC</mi>
<mrow>
<mi>T</mi>
<mi>p</mi>
<mi>e</mi>
<mi>t</mi>
</mrow>
</msub>
<mo><</mo>
<msub>
<mi>STC</mi>
<mrow>
<mi>D</mi>
<mi>R</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein λ is weight coefficient；
The order of severity of traffic conflict is judged according to R values.
8. the hybrid mainline toll station traffic conflict evaluation method according to claim 7 recognized based on automobile video frequency,
Characterized in that, working as R>When 1.15, it is determined as serious traffic conflict；When 0.9<R<When 1.15, it is determined as moderate traffic conflict；When
0.7<R<When 0.9, it is determined as slight traffic conflict.
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