title: Traffic Behavior Recognition Using the Pachinko Allocation Model.
abstract: CCTV-based behavior recognition systems have gained considerable attention in recent years in the transportation surveillance domain for identifying unusual patterns, such as traffic jams, accidents, dangerous driving and other abnormal behaviors. In this paper, a novel approach for traffic behavior modeling is presented for video-based road surveillance. The proposed system combines the pachinko allocation model (PAM) and support vector machine (SVM) for a hierarchical representation and identification of traffic behavior. A background subtraction technique using Gaussian mixture models (GMMs) and an object tracking mechanism based on Kalman filters are utilized to firstly construct the object trajectories. Then, the sparse features comprising the locations and directions of the moving objects are modeled by PAMinto traffic topics, namely activities and behaviors. As a key innovation, PAM captures not only the correlation among the activities, but also among the behaviors based on the arbitrary directed acyclic graph (DAG). The SVM classifier is then utilized on top to train and recognize the traffic activity and behavior. The proposed model shows more flexibility and greater expressive power than the commonly-used latent Dirichlet allocation (LDA) approach, leading to a higher recognition accuracy in the behavior classification.
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Suwon 446-701, Korea. thienht@oslab.khu.ac.kr.
authors.forename: Thien
authors.initials: T
authors.lastname: Huynh-The
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Suwon 446-701, Korea. oresti@oslab.khu.ac.kr.
authors.forename: Oresti
authors.initials: O
authors.lastname: Banos
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Suwon 446-701, Korea. lebavui@oslab.khu.ac.kr.
authors.forename: Ba-Vui
authors.initials: BV
authors.lastname: Le
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Suwon 446-701, Korea. mao.bui@khu.ac.kr.
authors.forename: Dinh-Mao
authors.initials: DM
authors.lastname: Bui
authors.affiliation: Department of Multimedia Science, Sookmyung's Women University, Seoul 140-742, Korea. yiyoon@sookmyung.ac.kr.
authors.forename: Yongik
authors.initials: Y
authors.lastname: Yoon
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Suwon 446-701, Korea. sylee@oslab.khu.ac.kr.
authors.forename: Sungyoung
authors.initials: S
authors.lastname: Lee
mesh_terms: D001334:Automobile Driving / Q000706:statistics & numerical data*; D001519:Behavior / Q000145:classification*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D014741:Video Recording
keywords: closed-circuit television (CCTV) system; pachinko allocation model; traffic behavior modeling; video-based road surveillance