Robust object tracking with multiple basic mean shift tracker

We propose a novel tracking algorithm which can work robustly under complex dynamic scenarios. Our algorithm is based on a scheme of multiple basic mean shift tracking. In this scheme, we use Sparse Principal Component Analysis to generate multiple target models, with which each basic mean shift tracker runs in parallel at the same time. The best configuration of a target is obtained by the weighted linear combination of its basic results. In addition, for the problem that the histogram of gradient under the mean shift tracking framework is easy to fall into local maxima, we introduce the histogram of Gradient Vector Flow to represent the target. Experimental results show that our tracker is able to handle severe appearance change and recover from drifts in realistic videos. The algorithm proposed in this paper can track the target accurately and reliably compared with other existing state-of-the-art tracking algorithms.