1. BACKGROUND 1-1 Traffic Flow Surveillance

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1 Auo-Recogniion of Vehicle Maneuvers Based on Spaio-Temporal Clusering. BACKGROUND - Traffic Flow Surveillance Conduced wih kinds of beacons mouned a limied roadside poins wih Images from High Aliude Plaforms Deparmen of Civil Engineering Universiy of Tokyo Takashi FUSE, Hidenori YODA, Eihan SHIMIZU, Morio TSUTSUMI fuse@planner..u-okyo.ac.jp Only provide poin measures such as flow, occupancy, velociy a paricular poins Canno observe he exac rajecory of each vehicle Lacks imporan informaion for policies decision on raffic problems - Measuremen wih Fixed Video Cameras -3 Usefulness of Images from High Aliude Plaforms Beacons Fixed Video Cameras High Aliude Plaforms Wide Area Exac Trajecories Field of view is narrow Limied applicaions o raffic engineering -4 Comparison among Various Resoluion Images In Fuure Uilizaion for Telecommunicaion Earh observaion Unmanned airship kep a a sraospheric level Resoluion 30m m 0cm LANDSAT-TM IKONOS HDTV aerial image (Aero Asahi Co. Ld. Coninuous observaion a wide areas Expeced o resul in a high resoluion image (0cm Grea poenial wih wider scope of uilizaion for raffic flow surveillance Yomiuri newspaper

Hidenori YODA, Eihan SHIMIZU, Morio TSUTSUMI fuse@planner..u-okyo.ac.

2 Observaion of exac rajecory of each vehicle in wide area.objecive Exploraion of he possibiliy of new raffic flow surveillance Poenial Applicaion in Traffic Engineering Dealing wih raffic congesion Tracking ou exac origin-desinaion Examinaion of signal conrol Examinaion of raffic conrol Simulaion of vehicle maneuvers Developmen of a new echnique for recogniion of vehicle maneuvers wih sequenial images from high aliude plaforms Discussion of he influence of spaial and emporal resoluion on vehicle recogniion 3. General Image Processing Approach Vehicle deecion in each image Vehicle Tracking by making a correspondence of deeced vehicles Vehicle Deecion Vehicle Tracking Segmenaion Sereo Maching Color segmenaion, Thresholding, Templae maching, Edge deecion, Color exracion, Feaure-based maching, Maching Probabilisic relaxaion mehod, Templae maching, Gradien-based approach, Srucure maching, Hough ransformaion Gradien-based approach, PIV(Paricle Image Velocimery Hough ransformaion Cross-correlaion mehod, Classificaion Spring model echnique, Maximum likelihood classifier, Delaunay essellaion Minimum disance classifier, ec. Clusering, Relaxaion mehod Ohers Snakes, Differeniaion ec. Applicaion of a Basic Mehod ex 4.Developmen of Vehicle Recogniion Mehod 4- Vehicle Recogniion Vehicle in an image Resul by color segmenaion Use of movemen as informaion Limiaion: enough informaion canno be derived from single image Vehicle recogniion in spaio-emporal image

conrol Examinaion of raffic conrol Simulaion of vehicle maneuvers Developmen of a new echnique for recogniion of vehicle maneuvers wih sequenial images from high aliude plaforms Discussion of he

3 Vehicle recogniion in spaio-emporal image 4- Spaio-Temporal Clusering Employ ( xyrgbuv,,,,,,, as feaures Original informaion in spaio-emporal images Locaion TimeColor xyrgb,,,,, ( Esimae Velociy ( uv, apply emplae maching Velociy Esimaion Unify pixels which have similar color Specify he regions as emplaes Templae maching wih before and afer frames Clusering l ( ( v ( ( ( { } {( ( } { } ( d = k x x + y y + k u u + v v αβ α β α β α β α β + k rα rβ + gα g + bα bβ + k α c β β d disance beween pixels αβ xyuvrgb,,,,,,, k,,, l kv kc k feaures of pixels weighs Pixels are assigned o he cluser o which i has he minimum disance Problem of Clusering Disconinuiy of clusers in ime direcion Even if here is only one vehicle, wo clusers migh be formed x Such clusers should be merged based on hisory of velociy and color informaion y 4-4 Cluser Merging ( x, y ( x, y ( x, y ( x, y { } x = x + ( x x + ( x x ( x x 3 = 3x -3 x -+ x-3, y = y + ( y y + ( y y ( y y 3 = 3y -3y + y { } Esimae locaion according o hisory of velociy Clusers which have similar color disribuion are merged 3

x x + y y + k u u + v v αβ α β α β α β α β + k rα rβ + gα g + bα bβ + k α c β β d disance beween pixels αβ xyuvrgb,,,,,,, k,,, l kv kc k feaures of pixels weighs Pixels are assigned o he cluser o

4 4-5 Auomaic Labelling of Vehicle Clusers ClusersVehicles or Ohers Smoohness of Velociy ( x + 4, y +4 ( u (, + 3, v+ 3 x + 3 y+3 ( u (, +, v+ x + y + ( ( x, u+, v+ y + + ( u, v ( x, y N ( u+ u + ( v+ v = T Threshold Shape and Size of Secion in Spaial Domain (Circumscribed Recangle a b Raio of wo sidesb 4.0 a Size4.8m a b 80m u + u v + v + + < T 5.Experimens Applicaion o HDTV Spaial resoluion 0cm, Temporal resoluion /6s Number of frames 0 Vehicle velociy40km/h Applicaion o HDTV Spaial resoluion 0cm, Temporal resoluion /6s Number of frames 5 Vehicle velociy0~0km/h Examinaion of Temporal Resoluion Influence Spaial resoluion 0cm, Temporal resoluion /s Number of frames 4 Vehicle velociy40km/h Examinaion of Spaial Resoluion Influence Spaial resoluion 40cm, Temporal resoluion /6s Number of frames 0 Vehicle velociy40km/h 6.Conclusion The appropriaeness of general image processing mehod for vehicle recogniion (in single image is evaluaed. A new echnique for vehicle recogniion based on spaio-emporal clusering is developed. Effeciveness of spaio-emporal clusering is confirmed hrough applicaion o real sequenial images. The relaion beween vehicle recogniion and spaio-emporal resoluion is examined. 4

Experimens Applicaion o HDTV Spaial resoluion 0cm, Temporal resoluion /6s Number of frames 0 Vehicle velociy40km/h Applicaion o HDTV Spaial resoluion 0cm, Temporal resoluion /6s Number of frames 5

5 Fuure Work Background Subracion Inpu Image Background Image Background Subracion Image Require auomaic geomeric correcion for making background 5

SELF-EVALUATION FOR VIDEO TRACKING SYSTEMS

SELF-EVALUATION FOR VIDEO TRACKING SYSTEMS Hao Wu and Qinfen Zheng Cenre for Auomaion Research Dep. of Elecrical and Compuer Engineering Universiy of Maryland, College Park, MD-20742 {wh2003, qinfen}@cfar.umd.edu