Fine Scale Estimation for Correlation Filter Tracking

摘要

Focusing on the issue that Correlation Filter Trackers has poor performance in scale variations, a fine scale estimation approach is proposed. Firstly, we train a scale correlation filter using the target initial state. Secondly, the target is segmented according to its shape and then two subgraph correlation filters are respectively established. During tracking, we judge the trend of scale changes by the relative position changes of the subgraphs and the weights of the scale samples are offset. In this way, we obtain the coarse scale estimation of the target. Finally, we use Newton method to accurately estimate the scale of the target. Experiments show that the algorithm achieves more accurate scale estimation and effectively improves the tracking success rate.