A robust object tracking method based on sparse representation

While much progress has been made for object tracking in recent years, it is still a challenging problem to handle large change in motion, appearance, scale and pose variation. One of the main reasons is the lack of effective representation to account for appearance variation. For this issue a flexible method based on superpixel segmentation is applied to divide an image into several patches. Besides, under the framework of sparse code a discriminative model based on superpixel is proposed. Experimental results show that our method tracks the object accurately and reliably in realistic videos where the appearance and motion are drastically changing overtime.