期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 10, 期 2, 页码 1064-1076出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2013.2294156
关键词
Birth intensity estimation; Gaussian mixture probability hypothesis density (GM-PHD) filter; multi-target visual tracking (MTVT); mutual occlusion handling
类别
资金
- Research Grants Council of Hong Kong [CityU 118311]
- City University of Hong Kong [7008176]
- National Natural Science Foundation of China [61273286, 51175087]
Tracking multiple moving targets in a video is a challenge because of several factors, including noisy video data, varying number of targets, and mutual occlusion problems. The Gaussian mixture probability hypothesis density (GM-PHD) filter, which aims to recursively propagate the intensity associated with the multi-target posterior density, can overcome the difficulty caused by the data association. This paper develops a multi-target visual tracking system that combines the GM-PHD filter with object detection. First, a new birth intensity estimation algorithm based on entropy distribution and coverage rate is proposed to automatically and accurately track the newborn targets in a noisy video. Then, a robust game-theoretical mutual occlusion handling algorithm with an improved spatial color appearance model is proposed to effectively track the targets in mutual occlusion. The spatial color appearance model is improved by incorporating interferences of other targets within the occlusion region. Finally, the experiments conducted on publicly available videos demonstrate the good performance of the proposed visual tracking system.
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