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 PAM into 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.
Highlights
Human behavior analysis (HBA), an integral component of many video surveillance systems, is a research area that has recently attracting attention from the computer vision and artificial intelligence communities
The method was compared with similar approaches using standard latent Dirichlet allocation (LDA) [36] and Markov clustering topic mode (MCTM) [30] for topic modeling
Classifier using pachinko allocation model (PAM) and LDA are reported in Tables 3–5 for the vertical and in Tables 6–8 for the horizontal traffic dataset
Summary
Human behavior analysis (HBA), an integral component of many video surveillance systems, is a research area that has recently attracting attention from the computer vision and artificial intelligence communities. In surveillance applications involving people or vehicles, the behaviors can be analyzed based on the human postures [8,9,10,11], the object trajectories [12,13] and the tracking information [14]. This information can be combined to recognize more complex contexts, such as vehicle interactions [15,16], human interactions [17,18] and human to vehicle interactions [19]. Given the large amount of surveillance video data available from closed-circuit television (CCTV) systems and the real-time nature of surveillance applications, it is desirable to provide an automatic operating system that may reduce human intervention as much as possible
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