Lucas-Kanade tracking based on sparse representation
稀疏表示的Lucas-Kanade目标跟踪

In this paper, we propose a new object tracking algorithm applying sparse representation in the Lucas-Kanade image registration algorithm. The object state parameters are solved to realize precise tracking by minimizing the L1-norm of the alignment error. The object appearance is represented by the static template and the dynamic dictionary. The dynamic dictionary is obtained by updating the tracking result in each frame. The object can be rebuilt by the sparse representation of the templates in the dynamic dictionary. To deal with tracking drift caused by dictionary update, a two-stage iteration with the static template and the dynamic dictionary respectively is included in our method. Numerous experimental results show that the proposed method is quite effective to partial occlusions, appearance changes and illumination changes. Meanwhile the system is computational efficient and works in real time.