Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
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In this paper, a method is proposed, to solve correspondence problem under structured space which is installed multiple cameras. The correspondence between different cameras is an important task to use the multiple camera system. For solving this problem, the proposed method is consists of three steps which are detection of moving object, feature extraction and correspondence among different cameras. First step is to detect moving people by background subtraction from multiple background model. The temporal difference is used jointly to remove noise occurred from temporary change. The detected regions are divided using labeling as individual person. The second step is to segment the each person by a criterion with appearance and context information. The segmented regions in a person are estimated as Gaussian mixture model (GMM) for correspondence. The final step is process of correspondence between different cameras. A GMM from a camera is matched with another GMM from other cameras. A ratio of those GMMs is used as a criteria to identify same person. The experiment was performed with the specific scenarios in quantitative results.