Efficient Consistent Labeling in Visual Surveillance System with Multiple Cameras
Date Issued
2010
Date
2010
Author(s)
Chen, Yu-Sheng
Abstract
Visual surveillance in multi-camera system has attracted more interest in recent years. Using limited number of cameras to simultaneously track and correctly label as many people as possible becomes an important topic of research, with low-cost consideration. In this thesis, we propose a surveillance system that can robustly track and identify multiple humans, for general building environments. Rather than gathering all information into a central server every frame, we track and segment each observation from local single camera, and only sending necessary information to the central server for correspondence processing at necessary time. Thus our framework can achieve observation correspondence between multi-cameras with confidence levels as correspondence quality indices. After correspondence process, the tracked object information is stored into the target databases for solving people re-entering problem. Without assuming common ground plane is observed by all cameras, our labeling process, which hierarchically associates objects after correspondence to target databases with matching confidence orders, still can construct relevant and accurate labeling assignment. The people information is then updated to improve target databases and local tracking performance. Occlusion handling for multi-object tracking can effectively enhance labeling accuracy and reduce the error of appearance information extraction due to object overlapping. The proposed labeling system yields robust performance even in most partial occlusion cases. Finally, we conclude with experimental results in several real video sequences and their detailed analysis.
Subjects
visual tracking
consistent labeling
multi-target tracking
correspondence between multiple cameras
occlusion handling for multi-target tracking
Type
thesis
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