Incremental visual tracking via sparse discriminative classifier

Currently, visual object tracking is a core research area as it can be applied in many applications of computer vision. However, tracking of a visual object is a difficult task as it can go through different varying conditions like occlusion of the target object, appearance variation, illumination variation, etc. during the tracking process. An efficient and robust visual object tracking based on sparse discriminative classier (SDC) and principal component analysis (PCA) subspace representation is presented in this work. The PCA subspace representation modelled the appearance model of the target object and SDC separates the target object and background object very efficiently. The computational complexity is much better than the other existing methods in the literature. Both quantitative and qualitative analyses of different video sequences are done to compare the proposed tracking algorithm with the other existing tracking algorithms. The experimental results show that the proposed method outperforms the other existing tracking algorithms.

Automated summary

The paper presents an efficient and robust visual object tracking method based on sparse discriminative classifier and principal component analysis. Through comparisons with existing tracking algorithms using both quantitative and qualitative analyses, the proposed method outperforms them.