It is a challenging problem to detect human and recognize their behaviors in video sequence due to the variations of background and the uncertainty of pose, appearance and motion. In this paper, we propose a systematic method to detect the behavior of tailgating. Firstly, in order to make the tracking process robust in complex situation, we propose an improved Gaussian Mixture Model (IGMM) for background and combine the Deterministic Nonmodel-Based approach with Gaussian Mixture Shadow Model (GMSM) to remove shadows. Secondly, we have developed an algorithm of object tracking by establishing tracking strategy and computing the similarity of color histograms. Having known door position in the scene, we specify tailgating behavior definition to detect tailgater. Experiments show that our system is robust in complex environment, cost-effective in computation and practical in real-time application.
In REF , the authors coped with challenges regarding video analysis for detecting tailgating behavior, such as variations of background and pose uncertainty, by an improved Gaussian Mixture Model (IGMM) for background.
A REAL-TIME TRACKING SYSTEM FOR TAILGATING BEHAVIOR DETECTION
VISAPP
Computer Science