Authors:
Yingxiang Zhang
;
Qiang Chen
and
Yuncai Liu
Affiliation:
Shanghai Jiaotong University, China
Keyword(s):
IGMM, Color histogram similarity, Tailgating detection.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Model-Based Object Tracking in Image Sequences
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Segment Cluster Tracking
;
Tracking of People and Surveillance
Abstract:
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.