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Proceedings Paper

Adaptive object tracking via both positive and negative models matching
Author(s): Shaomei Li; Chao Gao; Yawen Wang
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Paper Abstract

To improve tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, object tracking is posed as abinary classification problem and is modeled by partial least squares (PLS) analysis. Secondly, tracking object frame by frame via particle filtering. Thirdly, validating the tracking reliability based on both positive and negative models matching. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm can not only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences.

Paper Details

Date Published: 4 March 2015
PDF: 6 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94430R (4 March 2015); doi: 10.1117/12.2179209
Show Author Affiliations
Shaomei Li, National Digital Switching System Engineering and Technological Research and Development Ctr. (China)
Chao Gao, National Digital Switching System Engineering and Technological Research and Development Ctr. (China)
Yawen Wang, National Digital Switching System Engineering and Technological Research and Development Ctr. (China)


Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)

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