Boosting weak classifiers for visual tracking based on kernel regression

Bo Ma; Weizhang Ma

doi:10.1117/12.902887

8 December 2011 Boosting weak classifiers for visual tracking based on kernel regression

Bo Ma, Weizhang Ma

Proceedings Volume 8003, MIPPR 2011: Automatic Target Recognition and Image Analysis; 80031D (2011) https://doi.org/10.1117/12.902887
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

This paper proposes an online learning boosting method based on kernel regression for robust visual tracking. Although much progress has been made in using boosting for tracking, it remains a big challenge to get a robust tracker that is insensitive to illumination change, clutter, object deformation, and occlusion. In this paper, we use a nonlinear version of the recursive least square (RLS) algorithm so as to derive weak classifiers for visual tracking, which performs linear regression in a high-dimensional feature space induced by a Mercer kernel. In order to alleviate the computational burden and increase efficiency, we apply online sparsification to filter samples in feature space. In our boosting framework, adaptive linear weak classifiers are performed, the form of which is modified adaptively to cope with scene changes in every frame. Experimental results demonstrate that our proposed method has advantages in dealing with complex background in visual tracking, and often outperforms the state of the art on the popular datasets.

Citation Download Citation

Bo Ma and Weizhang Ma "Boosting weak classifiers for visual tracking based on kernel regression", Proc. SPIE 8003, MIPPR 2011: Automatic Target Recognition and Image Analysis, 80031D (8 December 2011); https://doi.org/10.1117/12.902887

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KEYWORDS

Optical tracking

Detection and tracking algorithms

Associative arrays

Video

Atomic layer deposition

Nonlinear filtering

RGB color model

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