Patch-based Scale Calculation for Real-time Visual Tracking

Robust scale calculation is a challenging problem in visual tracking. Most existing trackers fail to handle large scale variations in complex videos. To address this issue, we propose a robust and efficient scale calculation method in tracking-by-detection framework, which divides the target into four patches and computes the scale factor by finding the maximum response position of each patch via color attributes kernelized correlation filter. In particular, we employ the weighting coefficients to remove the abnormal matching points and transform the desired training output of the conventional classifier to solve the location ambiguity problem. Experiments are performed on several challenging color sequences with scale variations in the recent benchmark evaluation. And the results show that our method outperforms state-of-the-art tracking methods while operating in real-time.