Abstract
This paper proposes a new visual tracking method by constructing the robust appearance model of the target with convolutional sparse coding. First, our method uses convolutional sparse coding to divide the interest region of the target into a smooth image and four detail images with different fitting degrees. Second, we compute the initial target region by tracking the smooth image with the kernel correlation filtering. We define an appearance model to describe the details of the target based on the initial target region and the combination of four detail images. Third, we propose a matching method by the overlap rate and Euclidean distance to evaluate candidates and the appearance model to compute the tracking results based on detail images. Finally, the two tracking results are separately computed by the smooth image, and the detail images are combined to produce the final target rectangle. Many experiments on videos from Tracking Benchmark 2015 demonstrate that our method produces much better results than most of the present visual tracking methods.
Highlights
Visual tracking is a hot topic in computer vision and graphics
We evaluate the tracking results based on the accuracy of center position error and the success rate
The center accuracy is obtained by the center distance between the tracked result and the ideal target region, and the success rate is computed by the overlap rate of them
Summary
Visual tracking is a hot topic in computer vision and graphics. The changes in background and object bring many tracking challenges such as deformation, occlusion, rotation, and so on. The tracker based on support vector machine (SVM) [6] distinguishes the target and background by learning positive and negative samples. Based on MOSSE, Henriques et al [10] introduced the cyclic matrix and kernel method of tracking and convolving dense samples with the cyclic matrix formed by the target template in the Fourier domain They proposed the circulant structure kernel (CSK) tracking algorithm. Wang et al [17] proposed a SiamFC-based tracker using “rough matching” and “fine matching.” They enhanced tracking robustness through training in rough matching and improved discrimination through distance learning network in fine matching. The proposed tracker is achieved by combining the tracking results of the two parts to cope with challenges and improve the tracking performance
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