Scale adaptive compressive tracking

Abstract

Recently, the compressive tracking (CT) method (Zhang et al. in Proceedings of European conference on computer vision, pp 864–877, 2012) has attracted much attention due to its high efficiency, but it cannot well deal with the scale changing objects due to its constant tracking box. To address this issue, in this paper we propose a scale adaptive CT approach, which adaptively adjusts the scale of tracking box with the size variation of the objects. Our method significantly improves CT in three aspects: Firstly, the scale of tracking box is adaptively adjusted according to the size of the objects. Secondly, in the CT method, all the compressive features are supposed independent and equal contribution to the classifier. Actually, different compressive features have different confidence coefficients. In our proposed method, the confidence coefficients of features are computed and used to achieve different contribution to the classifier. Finally, in the CT method, the learning parameter λ is constant, which will result in large tracking drift on the occasion of object occlusion or large scale appearance variation. In our proposed method, a variable learning parameter λ is adopted, which can be adjusted according to the object appearance variation rate. Extensive experiments on the CVPR2013 tracking benchmark demonstrate the superior performance of the proposed method compared to state-of-the-art tracking algorithms.