Adaptive object tracking via both positive and negative models matching

Shaomei Li; Chao Gao; Yawen Wang

doi:10.1117/12.2179209

4 March 2015 Adaptive object tracking via both positive and negative models matching

Shaomei Li, Chao Gao, Yawen Wang

Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 94430R (2015) https://doi.org/10.1117/12.2179209
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China

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.

Citation Download Citation

Shaomei Li, Chao Gao, and Yawen Wang "Adaptive object tracking via both positive and negative models matching", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94430R (4 March 2015); https://doi.org/10.1117/12.2179209

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