| Abstract |
| Recently, the computer vision technology for video surveillance applications has made tremendous progress. Those applications may be roughly classified into single-camera systems and multi-camera systems. For a single-camera system, object labeling is an essential step for advanced analysis, like behavior understanding. However, a 2-D image lacks the depth information and thus the detection of moving targets usually suffers from the occlusion problem. The occlusion problem makes it difficult to correctly label or segment connective targets. Moreover, a supervised setting of targets number is usually needed for labeling. Unfortunately, this information is usually not available in practical applications. On the other hand, for a multi-camera system, object correspondence is crucial. The cross reference of multiple camera views may ease the occlusion problem and provide a more reliable way for object labeling. In this system, the major focus is to propose a unified method to label and map targets over multiple cameras. The proposed method can systematically estimate the target number, tackle inter-target occlusions problem, and require neither isolated foreground extraction nor color calibration. |