Effective Video Analysis for Red Light Violation Detection

Transcription

1 J. Basc. Appl. Sc. Res., 3(1s) , , TextRoad Publcaton ISSN Journal of Basc and Appled Scentfc Research Effectve Vdeo Analyss for Red Lght Volaton Detecton Maryam Hedar 1 and S. Amrhassan Monadjem 2 1 MSc student, Department of Telecommuncaton Engneerng, Ahar branch, Islamc Azad Unversty, Ahar, Iran 2 Assocate Professor, Department of Computer Engneerng Faculty of Engneerng Unversty of Isfahan Receved: June Accepted: July ABSTRACT Red lght Volaton detecton s one of the exgent branch of knowledge n modern ntellgent transportaton system, t s preferable to tradtonal detecton methods n operaton and mantenance aspect. Accordng to the practce of crossroads and the characterstcs of vehcles runnng red-lght, ths paper presents the methods of red lght runners vdeo detecton algorthms sate the real tme requrement as well as red-lght volaton detecton wth a hgh accuracy. In addton, they are also capable of toleratng a number of antagonstc but realstc stuaton such as pseudomotons due to shadows, poor contrast, mnmum number of traffc lght, pedestran motons and turnng vehcles. Purely from the vdeo, the new methods frstly construct a traffc lght sequence, and then detecton regon to detect movng vehcles and vehcles motons beyond the stop lne whle the lght s red. Our methods are smple and effectve, as whle as can solve the problems of undetected and wrongly detected vehcles seen n exstng algorthms. KEYWORDS: Red-Lght Runner, Vehcle Detecton, Vehcle Trackng, Frame Dfference, Regon to Detect. 1. INTRODUCTION One of the sgnfcant ssues whch reduces road safety s dsobeyng the traffc sgnals at sgnalzed. Statstcs from many countres showed that a hgh percentage of serous road accdents occurred at road junctons, and a hgh percentage of these are due to drvers dsobeyng or runnng the red lght [1]. Amng to deter red lght runnng, t s very mportant to develop an effcent red lght runnng detecton system called the red lght camera (RLC). It unnterruptedly montors the traffc n the crossroads and gets the evdence of vehcles runnng red-lght wthout human nference. Compare to the tradtonal detecton technology, such as nducton cols, radar, ultrasonc, and polce montors the vdeo detecton method has got advantages of easy mantenance, long servce lfe, hgh accuracy of detecton, real-tme detecton, and would not requre the demolton of pavements and roads. Amng to dssuade red lght runnng, dgtal magng, and sensor technologes have been developed and ntegrated for detectng the volaton n acton and takng photographc records of the accdents. Ths endeavor resulted n a class of systems called the red lght camera (RLC), whch operates n conjuncton wth the traffc lght control crcut and nductve loop detectors (ILD) bured under the surface of the road at the [2], [3]. In essence, such system must be electroncally connected to the traffc lght control system and the ILD bured under the road surface, usually beyond the stop lne. It s typcally mounted on a post at some dstance from the, wth a recordng camera vewng from contnuously the traffc drecton. The feld of vew (FOV) of the camera must nclude at least one traffc lght (typcally two), the and the vehcle s lcense plate for later prosecuton. The addtonal functons of the system are: 1) Handlng sgnals from the traffc lght and ILD; 2) Actvatng the camera; 3) Recordng other relevant nformaton of the offence scene. When the traffc lght s red and the ILD sense the presence of vehcle(s) on ether lane, the camera wll be trggered to take two photographs of about 1 sec apart. The frst photo depcts the vehcle enterng the beyond the stop lne, and the second photo shows the vehcle leavng the. Detaled nformaton such as date, tme, camera dentty, lane number, red lght duraton, and detecton dentty wll also be prnted on the photographs for future reference. At present, many RLC systems are gradually adoptng dgtal technologes for recordng, transmsson, and pattern recognton. However, the RLC based on ILD have some defcences, such as demolton of road surface, nflexblty, and the hgh cost. Due to these dsadvantages, the applcaton of ths knd of RLC s very dffcult. To mprove the effectveness of deterrng red lght runners, the RLC prncples must be redefned. One possble approach s to apply vdeo technology and vdeo analyss technques approprately, as vdeo contans a large amount of spatal and temporal nformaton that have not been fully exploted so far. Furthermore, vdeo technology and vdeo analyss theores are maturng rapdly. Therefore, t s our ntenton to study and explot ths possblty n ths paper and propose several solutons and novel methods that performs automatc RLR detecton on the traffc vdeo wthout needng any sgnals from the traffc lght system or the ILD. At frst method prmarly [4] Nelson Yung and Andrew La tackle the problem at two stages: 1) It detects and constructs a traffc lght sequence at the. 2) It estmates vehcle motons beyond the stop lne, whle the lght s red. The traffc lght sequence detecton frstly locates all the traffc lghts n the FOV by searchng all the red/yellow/green regons wth an aspect rato close to unty and smlar szes. The spatal relatonshp between the lghts s utlzed to elmnate other regons, whle the lght s temporal relatonshp s used to mprove the confdence of the former decson and to construct the lght sequence. As there may be more than one traffc lght under the FOV, the traffc lght wth the largest dmenson,.e., closest to the camera, s then chosen for all the subsequent analyss. Correspondng Author: Maryam Hedar, MSc student, Department of Telecommuncaton Engneerng, Ahar branch, Islamc Azad Unversty, Ahar, Iran. Emal address: m-hedar@au-ahar.ac.r 642

2 Hedar et al.,2013 Ths method s developed to replace the traffc lght control sgnal. The detecton of vehcle moton beyond the stop lne s bult on a drecton-based moton estmaton algorthm. In ths case, drecton-based three-step search algorthm s developed upon the concept of vrtual loop detectors (VLD). The concept of VLD permts regons to be defned on the vdeo that can emulate the functon of the physcal ILD. By defnng these VLD beyond the stop lne, ther motons along the road drecton may be estmated. In prncple, motons objects such as pedestrans, turnng vehcles and shadows may be dfferentated accordng to ther moton vectors. Based on ths method, a prototype has been mplemented and evaluated usng a number of traffc vdeos taken under dfferent outdoor condtons, ncludng one wth the vdeo camera mounted on an exstng RLC post. In all our evaluatons, the method dentfed all the RLR correctly. Also t was observed that the new method s capable of toleratng a number of hostle but realstc stuatons: 1) mnmum number of traffc lght; 2) pseudomotons due to shadows; 3) poor contrast; 4) pedestran motons; and 5) turnng vehcles. Second method s explaned n the that many RLC systems based on vdeo analyss technques use vrtual loop detectors (VLD) n the vdeo pcture nstead of ILD under the road surface. The concept of VLD permts regons to be defned on the vdeo, and can emulate the functon of the physcal ILD. In real world, f the settng of the VLD s not sutable, red lght runners would be undetected, whle t would wrongly detect rght-turnng vehcles as runners. In [5] Chen Yanyue and Yang Chenhu present a novel method that detects the red lght runners by analyzng the tracks of vehcles. The method s on the bass of movng objects detecton and trackng. After that, the movng ponts can be obtaned. Ths method prmarly tackles the problems n the exstng VLD based on RLC such as: 1) undetected f the VLD can not cover the drvng regon of the vehcle(s) and 2) wrongly detected when the vehcle(s) go through the prohbted zoon but turnng rght. In [6] D Luo et al. explan that the man methods of vdeo detecton are feature lne method, vrtual col technology, movng target trackng technology, and vrtual col technology cooperatng wth vehcle trackng technology. Feature lne method greatly depends on lane condton and s easy to be affected by foot passenger, llumnaton, etc. There are threehard problems n vrtual col technology and movng target trackng technology, whch are hard to segment movng target from background accurately, hard to track movng target accurately and hard to gve the rght judgment for movng target behavor. Those problems result n hgh error detecton rate and mss detecton rate exstng n red-lght volaton vdeo detecton system. Although vrtual col technology cooperatng wth vehcle trackng technology can mprove detecton rate, the algorthm s complex.then s presented a vson vehcle detecton algorthm based on regon accordng to the practce of crossroads and the characterstcs of vehcles runnng red -lght. In ths paper, we study several red-lght volaton detecton methods and then present a vson vehcle detecton algorthm can elmnate the dsturbance aroused by llumnaton, shadow, foot passenger, etc, therefore our method s smple and effectve, and the experment results show that ths algorthm, satsfes the real tme requrement and detects red-lght volaton ncdents wth a hgh accuracy. The organzaton of ths paper s as follows, secton 2 we present full procedure for red lght runners vdeo detecton, n ths secton we detals the algorthms for red lght runners vdeo detecton based on analyss of tracks of vehcles. In secton 3 presents the results dscusson the performance of the our methodology 2. Full Procedure for Red Lght Runners Vdeo Detecton Based on Analyss of Tracks of Vehcles Wth reference to Fg. 1, the traffc lght detecton and judgment s the frst part of the system because of the future works need to be done only when the traffc lght s red. Second, the nput vdeo s automatcally analyzed by the vehcle moton estmaton and trackng n the feld of detecton (FOD) whch defne as the zoon around stop lne amng to obtan movng ponts. Ft movng ponts so as to provde reference to analyze tracks of vehcles. Fg. 1. Functonal Block Dagram of The System It should be noted that the stop lne only needs to be detected once. As the camera parameters are fxed, the locaton of the stop lne s fxed, whch s used to defne the locaton of the FOD and to detect the red lght runners. The prohbted zone can be weakly defned as the area between the entrance and the ext of a, bounded by the stop lnes Fg

3 J. Basc. Appl. Sc. Res., 3(1s) , 2013 Fg. 2. Stop Lnes and Prohbted Zone [4] To detect the stop lne, many detecton and segmentaton methods may be employed [7]. In general, a frame where the stop lne s not obstructed must be obstructed must be obtaned frst. Ths usually would be a background frame obtaned from background estmaton methods. The stop lne s detected usng Hough transform n ths method. In general, a traffc lght conssts of three separate lghts n red, yellow, and green spatally algned vertcally from top to bottom n ths order. As t s lkely that there wll be smlar red, yellow, and green objects n the FOV, the frst task n detectng the traffc lghts s to solate the lghts from a multtude of smlar color regons. Of the three colors, t s observed that green s the most dffcult to solate because of ts varaton n shape, and the presence of smlar green regons such as trees and bushes around a typcal. Therefore, t would probably be more effcent f the detecton starts wth the red and yellow. In addton to the spatal relatonshp as descrbed, there s also a temporal relatonshp governng the traffc lghts. The argument for utlzng ths temporal relatonshp n the detecton s that outdoor envronment s hostle. For nstance, any sudden change n the ambent lght level may affect the brghtness of the lghts, especally the green lght and may ntroduce ambguty. Therefore, trackng the lght sequence would probably provde a more stable and robust soluton [8] Our method uses frame-to-background dfferences n hue algorthm to judge whether detecton regon s covered by vehcle, the qualty of subtracton result affects the accuracy of latter judgment drectly. So how to construct "pure" background model s the key to mprove veracty of detecton. The background s constantly changng by the effect of tme, lght, weather condtons, etc, so t needs a knd of algorthm that t s able to conduct a background template automatcally [9]. In Surendra background updatng algorthms, the regon whch has movng objects s found by usng frame subtracton, It updates background by remanng the background for the regon whch has movng objects and replacng the background wth the current frame for the regon whch has no movng objects. After a perod of tme, all the background of nterested regon wll be updated. In ths paper, t wll take a long tme to capture the mage whch no movng object beng on detecton regon owng to the length of detecton regon. So the detecton regon was dvded nto three parts and update background of each part dvdually. Fg. 3. Background Update Algorthm Flowchart for Part [5] Fg. 3 s the background update algorthm for part of detecton regon [10]. The algorthm detects effcacously and can elmnate the dsturbance aroused by llumnaton, shadow, foot passenger, etc, whch satsfes the real tme and accuracy requrement for traffc montorng system. Consderng the complex envronment of crossroads and dense vehcles, the algorthm s not well to deal wth the problem of hded vehcles. The movng ponts of vehcle can be obtaned by dong vehcle moton estmaton and trackng [11]. Each movng pont s the centrod of the vehcle whch s the result of trackng. If the movng ponts s mapped from background to the X-Y space Fg. 1, them can be defned as a sequence: (( x 1, y1 ), ( x2, y 2 ),...,( x n, y n )) (1) 644

4 Hedar et al.,2013 where n denotes the number of movng ponts and x, y respectvely denotes value of the no. movng pont n X-axs and Y-axs. To analyze the track of the vehcle, that for every y there s a x correspondng to t, that s the value of sequence (1) s contnuous n Y-axs. Because the poston ponts of the mage are nteger, was defned that y 1 y 1, y y1, y n and y 1 s the value of Y-axs for the frst pont of the movng ponts and y n s the last one. That movng ponts are not contnuous n Y-axs owng to choose of nter frame space and the change of vehcle speed. In a word, movng ponts fttng needs to be done before analyze tracks of vehcles. In ths method, cubc splnes nterpolaton was adopted to do the work [12]. The Y ( y 1, y2,..., yn ) s supposed as a gven data, accordng to the relatonshp between x and y, the cubc splnes functon S(x) can be obtaned, where Y s ndependent varable and X s dependent varable all the vehcle whch trough the red lne durng ths perod are red lght volaton, t ncludes three stuaton below: 1. When the vehcle goes to straght t s determned for a red lght volaton. 2. When the vehcle turns left t s determned for a red lght volaton lke stuaton When the vehcle turns rght t s not determned for a red lght volaton Fg. 4. Fg. 4. Three Stuaton n The Cross Juncton The descrpton of the algorthm as below: 1. To detect the vehcle L whether or not s a red lght volaton, the sequence of movng ponts s analysed of t: {( x L 1, y L 1 ), ( x L 2, y L 2 ),..., ( x Ln, y Ln )} (2) If there beng y m y lne, m 1, n, the vehcle L would be a red lght volaton and go on to the step 2; 2. To analyze the sequence {( x L1, y L1), ( x L2, y L2 ),...,( x Ln, y Ln )} For every y y m, ( m 1, m 2,..., n) : a) Along wth the aggrandzement of value of Y-axs, the change of value of X-axs s abnormty and extent of change s less than T (the threshold value of change), t shows that the stuaton of the vehcle L s gong to straght and t s a red lght volaton; b) Along wth the aggrandzement of value of Y-axs, the value of X-axs s ncrease and extent of ncrease s bgger than T, t shows that the vehcle turns left and t s a red lght volaton also; c) Along wth the aggrandzement of value of Y-axs, the value of X-axs s reduce and extent of reduce s bgger than T, t shows that the vehcle turns rght and t sn t a red lght volaton. The experment results show that the algorthm not only satsfes the real tme requrement but also detects red-lght runners wth a hgh accuracy, and has been able to elmnate dsturbng factors such as lghtng and clmate changes. 3. RESULTS DISCUSSION A prototype was mplemented based on the above methodology. The fnal result of vehcles moton estmaton n Fg. 5. The rectangles are show the vehcles whch are detected. The tral on vehcle moton estmaton shows that the method we used n ths paper s able to meet our requrements. Fg. 5. Results of The Method 645

5 J. Basc. Appl. Sc. Res., 3(1s) , 2013 The red lght runners detecton algorthm proposed n ths paper had been tested on vdeo sequences of the crossroad juncton, and the results of the tral showed that our method can tracks vehcles before the stop lne, dstngushes from the stuatons n traffc, and detects red lght runners correctly and easly. Conclusons In ths study, we have presented a novel and effectve methods for detectng red lght runners n ths paper. A number of method have been observed. Frstly, these methods offers a moble, robust, and cost-effectve soluton, and the set up process was smple and fast. Secondly, the correct detecton rate was acceptable and can solve the problem of undetected and wrongly detected runner n the tradtonal methods. Next, the vdeo and the tracks of vehcles analyss can be further developed nto a multfunctonal archtecture where other traffc parameters may be calculated or estmated. REFERENCES 1. Lum, K.M., Wong, Y.D., (1998): An overvew of red-lght survellance cameras n Sngapore, ITE J. Web, 1, Chn, H.C. (1989): Effect of automatc red lght cameras on red-runnng, Traffc Eng. Contr., vol. 30(4), Thompson, S.J. and et al. (1989): Puttng red-lght volators n the pcture, Traffc Eng. Contr., 30(3), Yung, N., La, A. (2001): An effectve vdeo analyss method for detectng red lght runners, IEEE transacton on vehcular technology, 50(4), Yanyue, Chen and Chenhu, Yang, 2010, Vehcle red-lght volaton detecton base on regon, Computer Scence and Informaton Technology (ICCSIT), 3rd IEEE Internatonal Conference, 9, Dgtal Object Identfer: /ICCSIT , Luo, D. Huang, X., Qn, L. (2008): The Research of Red Lght Runners Vdeo Detecton Based on Analyss of Tracks of Vehcles, Computer Scence and Informaton Technology, ICCSIT '08. Internatonal Conference, Dgtal Object Identfer: /ICCSIT, 83, Shan, Zhu., Ka-Kuang, Ma. (2002): A new damond search algorthm for fast block-matchng moton estmaton, IEEE transacton on mage processng, 9(2), La, H.S., Yung, N.H.C. (1998): A system archtecture for vsual traffc survellance, 5th World Congress Intellgent Transportaton Systems, Seoul, Korea, paper armann, K., Brandt, A. (1990): Movng object recognton usng an adaptve background memory, Tmevaryng Image Processng and Movng Object Recognton, Elsever, Amsterdam, The Netherlands, Paper Chengzhong, Yu., Jun, Zhu., Xaohu, Y. (2005): Vdeo object detecton based on background subtracton, Journal of Southeast Unversty (Natural Scence Edton), 35, paper Stauffer, C., Grmson, WEL. (1999): Adaptve background mxture models for real-tme trackng, IEEE Internatonal Conference on Computer Vson and Pattern Recognton, 2, Cofman, B., Beymer, D., Malk, J. (1998): A real-tme computer vson system for vehcle trackng and traffc survellance, Transport. REs: Part C, 6(4),