A joint illumination and sparse representation for visual tracking
Suguo Zhu, Junping Du, Pengcheng Han · 2013
Tracking object under illumination conditions is an important task in computer vision. A large number of methods for tracking object are described in the literature. Unfortunately, there is not enough robust methods that work for all applications. We have therefore proposed a tracker for the changing lights conditions with a model of the combination of sparse representation and intensity feature of the video sequence. In addition, the model is an object instanced model and depends on the illumination of the surroundings, and thus is effective in tracking object in illumination conditions. Experimental results show that the proposed tracker works well under significant illumination changes and outperforms many state-of-the-art tracking algorithms.