Tracking video objects with feature points based particle filtering |
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Authors: | Tao Gao Guo Li Shiguo Lian Jun Zhang |
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Affiliation: | 1. Electronic Information Products Supervision and Inspection Institute of Hebei Province, Shijiazhuang, 050071, China 2. Industry and Information Technology Department of Hebei Province, Shijiazhuang, 050051, China 3. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China 4. France Telecom (Orange Labs) Beijing, Beijing, 100080, China 5. School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, China
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Abstract: | For intelligent video surveillance, the adaptive tracking of multiple moving objects is still an open issue. In this paper, a new multi-object tracking method based on video frames is proposed. A type of particle filtering combined with the SIFT (Scale Invariant Feature Transform) is proposed for motion tracking, where SIFT key points are treated as parts of particles to improve the sample distribution. Then, a queue chain method is adopted to record data associations among different objects, which could improve the detection accuracy and reduce the computational complexity. By actual road tests and comparisons, the system tracks multi-objects with better performance, e.g., real time implementation and robust against mutual occlusions, indicating that it is effective for intelligent video surveillance systems. |
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