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- Title
Tracking video objects with feature points based particle filtering.
- Authors
Gao, Tao; Li, Guo; Lian, Shiguo; Zhang, Jun
- 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.
- Subjects
VIDEO surveillance; MONTE Carlo method; SCALING laws (Statistical physics); COMPUTATIONAL complexity; NP-complete problems; DATA recorders &; recording
- Publication
Multimedia Tools & Applications, 2012, Vol 58, Issue 1, p1
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-010-0676-y