Online selection of discriminative features with approximated distribution fields for efficient object tracking

Abstract

This paper proposes an efficient tracking method to handle the appearance of object. Distribution fields descriptor (DF) which allows the representation of uncertainty about the tracked object has been proved to be very robust to illumination changes, image noise and small misalignments. However, DF tracking is a generative model that does not utilize the background information, which limits its discriminative capability. This paper improves the original DF tracking algorithm, and adopts layers of DF feature to represent the target instead of traditional Haar-like features. Also, the online discriminative feature selection algorithm at instance level helps select the discriminative DF layer features. Besides, approximating DF features with soft histograms helps to reduce the computation time greatly. Compared with the original algorithm and other state-of-the-art methods, the proposed tracking method shows excellent performances on test baseline dataset.