CN108198427A - Green light of rushing based on characteristics of image frame is broken rules and regulations determination method - Google Patents

Green light of rushing based on characteristics of image frame is broken rules and regulations determination method Download PDF

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Publication number
CN108198427A
CN108198427A CN201711242925.7A CN201711242925A CN108198427A CN 108198427 A CN108198427 A CN 108198427A CN 201711242925 A CN201711242925 A CN 201711242925A CN 108198427 A CN108198427 A CN 108198427A
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China
Prior art keywords
frame
image
green light
image frame
rushing
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CN201711242925.7A
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Chinese (zh)
Inventor
马杰
杜红民
王莹莹
孔晓阳
王茹川
王磊
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Central Plains Wisdom Urban Design Research Institute Co Ltd
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Central Plains Wisdom Urban Design Research Institute Co Ltd
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Priority to CN201711242925.7A priority Critical patent/CN108198427A/en
Publication of CN108198427A publication Critical patent/CN108198427A/en
Pending legal-status Critical Current

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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
    • 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
    • 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
    • G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)

Abstract

The present invention provides a kind of green light of rushing based on characteristics of image frame and breaks rules and regulations determination method, includes the following steps：Step 1, the live video stream for capturing equipment in front end is obtained based on B/S frameworks；Step 2, VideoStream To IMG functions are passed to using the video flowing address of the live video stream as parameter, obtain characteristics of image frame stream；Step 3, picture background modeling is carried out by Gaussian Background model；Step 4, the second frame of extraction characteristics of image frame stream, last frame and intermediate frame, and grey level histogram method is used to judge the similarity of three frame image features frames, export similarity value；Step 5, current traffic state at road cross is judged according to similarity value；Step 6, signal lamp state in characteristics of image frame is identified using deep learning algorithm；Step 7, judge to whether there is in characteristics of image frame stream according to signal lamp state and current traffic state at road cross and rush green light phenomenon；Step 8, it will determine that result and intermediate frame image characteristic frame upload to Snapshot System of Traffic Violation.

Description

Green light of rushing based on characteristics of image frame is broken rules and regulations determination method
Technical field
The present invention relates to image analysis technology field, specifically, relate to a kind of rush green light based on characteristics of image frame Determination method violating the regulations.
Background technology
As vehicle becomes increasingly popular, traffic congestion and traffic accident annoying urban transportation always, almost per minute to have Traffic accident is happened at the every nook and cranny in city, and traffic police department increased traffic specification dynamics, while almost each crossing in recent years There is candid photograph equipment violating the regulations, the behavior of numerous car owners has obtained specification.But with the continuous development of society, caused by rushing green light Traffic accident rate is riseing year by year, rushes green light and is：Obviously see that junction ahead has resulted in congestion, still crossed after green light Stop line drives towards intersection, the phenomenon that causing more congestion or even traffic accident.It is difficult sometimes to distinguish due to complicated condition Be normally travel or, rush green light behavior, therefore solve the problems, such as that judgement rushes green light and has important meaning to the improvement of urban transportation Justice.
In order to solve the problems, such as present on, people are seeking a kind of ideal technical solution always.
Invention content
To achieve these goals, the technical solution adopted in the present invention is：It is a kind of that green light is rushed based on characteristics of image frame Determination method violating the regulations, includes the following steps：
Step 1, the live video stream for capturing equipment in front end is obtained based on B/S frameworks；
Step 2, the video flowing address of the live video stream is passed to VideoStreamToIMG functions as parameter, obtained Take characteristics of image frame stream；
Step 3, picture background modeling is carried out by Gaussian Background model；
Step 4, the second frame of extraction characteristics of image frame stream, last frame and intermediate frame, and judge three using grey level histogram method The similarity of frame image features frame exports similarity value；
Step 5, current traffic state at road cross is judged according to similarity value；
Step 6, signal lamp state in characteristics of image frame is identified using deep learning algorithm, is green light in signal lamp state When represented with L=G；
Step 7, the current traffic state at road cross judgement figure obtained according to the signal lamp state and step 5 obtained in step 6 Green light phenomenon is rushed as whether there is in characteristic frame stream；
Step 8, it will determine that result and intermediate frame image characteristic frame upload to Snapshot System of Traffic Violation.
Based on above-mentioned, step 1, based on B/S frameworks obtain front end capture equipment live video stream the specific steps are：
Under B/S frameworks, the SDK interfaces that equipment is captured based on front end are called by video dedicated network, front end is obtained and grabs The live video stream of equipment is clapped, and preserves video flowing address StreamAd.
Based on above-mentioned, step 4 the specific steps are：
Extract the second frame image IMGArr [2], end-frame image IMGArr [i] and the intermediate frame image of characteristics of image frame IMGArr[i/2]；
Using grey level histogram method to the second frame image IMGArr [2], the end-frame image IMGArr [i] of characteristics of image frame and Intermediate frame image IMGArr [i/2] carries out similarity-rough set, output similarity value F (G, S, N)；
Wherein G, S are grey level histogram, and N is color space number of samples.
Based on above-mentioned, step 5 the specific steps are：
Variable V is enabled to represent current crossing state, current crossing state is judged according to the value of F (G, S, N)：
If F (G, S, N)≤70, then V=congestions；
If F (G, S, N)<70 and F (G, S, N)>30, then V=low running speeds；
If F (G, S, N)≤30, then V=is unimpeded.
Based on above-mentioned, the basis for estimation that green light is rushed in step 7 is：
As V=congestions and L=G, then it is judged as rushing green light；Otherwise it is judged as not rushing green light.
The present invention has prominent substantive distinguishing features and significant progress compared with the prior art, and specifically, the present invention is real When obtain front end capture equipment video flowing, and pass through Gaussian Background model method carry out picture background modeling, from video flowing It obtains characteristics of image frame and the state at current crossing is had determined that using grey level histogram hair progress similarity judgement, finally combine letter Signal lamp condition adjudgement has the characteristics that design science, easy to use, can greatly alleviate traffic with the presence or absence of rushing green light phenomenon Pressure.
Specific embodiment
Below by specific embodiment, technical scheme of the present invention is described in further detail.
The present invention provides a kind of green light of rushing based on characteristics of image frame and breaks rules and regulations determination method, includes the following steps：
Step 1, the live video stream for capturing equipment in front end is obtained based on B/S frameworks；
The SDK interfaces that equipment is captured based on front end are called by video dedicated network, front end is obtained and captures the real-time of equipment Video flowing, and video flowing address StreamAd is preserved, preserving multiple front ends using arrStreamAd [j] captures the real-time of equipment Video flowing address；
Step 2, the video flowing address of the live video stream is passed to VideoStreamToIMG functions as parameter, obtained Characteristics of image frame stream is taken, is represented with array IMGArr [i], wherein i is the index of current frame rate, and characteristics of image frame head frames are IMGArr [1], the second frame of characteristics of image frame, that is, IMGArr [2], the i-th/2 frame, that is, IMGArr [i/2] of characteristics of image frame, image are special Levy the i-th frame, that is, IMGArr [i] of frame；
Step 3, picture background modeling is carried out by Gaussian Background model；
Step 4, the second frame of extraction characteristics of image frame stream, last frame and intermediate frame, and judge three using grey level histogram method The similarity of frame image features frame, output similarity value F (G, S, N)：
Wherein G, S are grey level histogram, and N is color space number of samples；
Step 5, current traffic state at road cross is judged according to similarity value；
Variable V is enabled to represent current crossing state, current crossing state is judged according to the value of F (G, S, N)：
If F (G, S, N)≤70, then V=congestions；
If F (G, S, N)<70 and F (G, S, N)>30, then V=low running speeds；
If F (G, S, N)≤30, then V=is unimpeded；
Step 6, signal lamp state in characteristics of image frame is identified using deep learning algorithm, is green light in signal lamp state When represented with L=G；
Step 7, the current traffic state at road cross judgement figure obtained according to the signal lamp state and step 5 obtained in step 6 As, with the presence or absence of green light phenomenon is rushed, specific basis for estimation is in characteristic frame stream：As V=congestions and L=G, then it is judged as rushing green light； Otherwise it is judged as not rushing green light；
Step 8, capturing system violating the regulations is called to issue the data upwards transmission port on private network server, will determine that result and position It puts image and uploads to Snapshot System of Traffic Violation.
Specifically, in step 2 by Gaussian Background model method carry out picture background modeling the specific steps are：
Obtain the pixel value and depth value of each pixel in the first frame image of characteristics of image frame；
According to the pixel value and depth value of each pixel, variance and the expectation of the present image are calculated respectively Value；According to the variance and desired value of the present image, in the multiple single Gauss models included from preset mixed Gauss model The single Gauss model of one matching of selection；
According to the variance and desired value of the present image, the variance of the single Gauss model of matching corresponding to each pixel It is updated with desired value, to complete background modeling.
Finally it should be noted that:The above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof；To the greatest extent The present invention is described in detail with reference to preferred embodiments for pipe, those of ordinary skills in the art should understand that：Still It can modify to the specific embodiment of the present invention or equivalent replacement is carried out to some technical characteristics；Without departing from this hair The spirit of bright technical solution should all cover in the claimed technical solution range of the present invention.

Claims (5)

1. a kind of green light of rushing based on characteristics of image frame is broken rules and regulations determination method, which is characterized in that is included the following steps：
Step 1, the live video stream for capturing equipment in front end is obtained based on B/S frameworks；
Step 2, VideoStream To IMG functions are passed to using the video flowing address of the live video stream as parameter, obtained Characteristics of image frame stream；
Step 3, picture background modeling is carried out by Gaussian Background model；
Step 4, the second frame of extraction characteristics of image frame stream, last frame and intermediate frame, and three frame figures are judged using grey level histogram method As the similarity of characteristic frame, similarity value is exported；
Step 5, current traffic state at road cross is judged according to similarity value；
Step 6, signal lamp state in characteristics of image frame is identified using deep learning algorithm, is used when signal lamp state is green light L=G is represented；
Step 7, the current traffic state at road cross obtained according to the signal lamp state and step 5 obtained in step 6 judges image spy It whether there is in sign frame stream and rush green light phenomenon；
Step 8, it will determine that result and intermediate frame image characteristic frame upload to Snapshot System of Traffic Violation.
2. a kind of green light of rushing based on characteristics of image frame according to claim 1 is broken rules and regulations determination method, which is characterized in that step Rapid 1, based on B/S frameworks obtain front end capture equipment live video stream the specific steps are：
Under B/S frameworks, the SDK interfaces that equipment is captured based on front end are called by video dedicated network, front end candid photograph is obtained and sets Standby live video stream, and preserve video flowing address StreamAd.
3. a kind of green light of rushing based on characteristics of image frame according to claim 1 is broken rules and regulations determination method, it is characterised in that：Step Rapid 4 the specific steps are：
Extract the second frame image IMGArr [2], end-frame image IMGArr [i] and the intermediate frame image IMGArr of characteristics of image frame [i/2]；
Using grey level histogram method to the second frame image IMGArr [2], end-frame image IMGArr [i] and the centre of characteristics of image frame Frame image IMGArr [i/2] carries out similarity-rough set, output similarity value F (G, S, N)：
Wherein G, S are grey level histogram, and N is color space number of samples.
4. a kind of green light of rushing based on characteristics of image frame according to claim 3 is broken rules and regulations determination method, the specific step of step 5 Suddenly it is：
Variable V is enabled to represent current crossing state, current crossing state is judged according to the value of F (G, S, N)：
If F (G, S, N)≤70, then V=congestions；
If F (G, S, N)<70 and F (G, S, N)>30, then V=low running speeds；
If F (G, S, N)≤30, then V=is unimpeded.
5. a kind of green light of rushing based on characteristics of image frame according to claim 4 is broken rules and regulations determination method, green light is rushed in step 7 Basis for estimation be：
As V=congestions and L=G, then it is judged as rushing green light；Otherwise it is judged as not rushing green light.
CN201711242925.7A 2017-11-30 2017-11-30 Green light of rushing based on characteristics of image frame is broken rules and regulations determination method Pending CN108198427A (en)

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CN201711242925.7A CN108198427A (en) 2017-11-30 2017-11-30 Green light of rushing based on characteristics of image frame is broken rules and regulations determination method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711242925.7A CN108198427A (en) 2017-11-30 2017-11-30 Green light of rushing based on characteristics of image frame is broken rules and regulations determination method

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Citations (4)

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Publication number Priority date Publication date Assignee Title
US20020054210A1 (en) * 1997-04-14 2002-05-09 Nestor Traffic Systems, Inc. Method and apparatus for traffic light violation prediction and control
CN102039738A (en) * 2009-12-09 2011-05-04 辉县市文教印务有限公司 Page online fuzzy identification system of high-speed binding machine
CN102768801A (en) * 2012-07-12 2012-11-07 复旦大学 Method for detecting motor vehicle green light follow-up traffic violation based on video
CN103279765A (en) * 2013-06-14 2013-09-04 重庆大学 Steel wire rope surface damage detection method based on image matching

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020054210A1 (en) * 1997-04-14 2002-05-09 Nestor Traffic Systems, Inc. Method and apparatus for traffic light violation prediction and control
CN102039738A (en) * 2009-12-09 2011-05-04 辉县市文教印务有限公司 Page online fuzzy identification system of high-speed binding machine
CN102768801A (en) * 2012-07-12 2012-11-07 复旦大学 Method for detecting motor vehicle green light follow-up traffic violation based on video
CN103279765A (en) * 2013-06-14 2013-09-04 重庆大学 Steel wire rope surface damage detection method based on image matching

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
卢志茂等: "基于特征帧构建的运动目标检测方法", 《红外与激光工程》 *

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