Robust Visual Tracking Based On Convolutional Sparse Coding

This paper proposes a target tracking algorithm based on convolutional sparse coding (CSC). This algorithm first uses the CSC to divide the interest region into a smooth image and four detail images with different fitting degrees. Then, the smooth image is tracked to produce the initial result based on the kernel correlation filtering (KCF). According to the initial value of the target area and the linear combination of four detail images, the appearance model of the details is constructed. And the matching between samples and the appearance model is performed according to the overlap rate and Euclidean distance to determine the tracking results of the detail images. In the end, the two tracking results including the one based on the smooth image and the other one based on the detail images are linearly combined to determine the final position of the target in the new frame. Many experiments on the video sequences from Tracking Benchmark 2015 show that: our method produces much better result than most of present visual tracking methods.