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Sensors 2015, 15(7), 16040-16059; doi:10.3390/s150716040

Traffic Behavior Recognition Using the Pachinko Allocation Model

1
Department of Computer Engineering, Kyung Hee University, Suwon 446-701, Korea
2
Department of Multimedia Science, Sookmyung's Women University, Seoul 140-742, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Jesús Fontecha
Received: 14 April 2015 / Revised: 24 June 2015 / Accepted: 1 July 2015 / Published: 3 July 2015
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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. View Full-Text
Keywords: traffic behavior modeling; closed-circuit television (CCTV) system; pachinko allocation model; video-based road surveillance traffic behavior modeling; closed-circuit television (CCTV) system; pachinko allocation model; video-based road surveillance
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Huynh-The, T.; Banos, O.; Le, B.-V.; Bui, D.-M.; Yoon, Y.; Lee, S. Traffic Behavior Recognition Using the Pachinko Allocation Model. Sensors 2015, 15, 16040-16059.

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