Saliency Guided Visual Tracking via Correlation Filter With Log-Gabor Filter

Abstract

Correlation filter (CF) based tracking algorithms have tremendously contributed to the field of visual tracking due to the high computational efficiency and competitive performance. Nonetheless, most CF-based trackers are vulnerable to the influence of occlusion and boundary effect, which results in suboptimal performance. In this article, we propose saliency guided visual tracking via correlation filter with log-Gabor filter to robustify its performance under occlusion and boundary effect challenges. Firstly, we propose the CF with log-Gabor filter to get a robust appearance model. The log-Gabor filter is adopted to preprocess the sequence to gain the log-Gabor feature, which provides important cues for tracking since it encodes the texture information. Secondly, considering the prior information, we embed the novel saliency guided adaptive spatial feature selection to filter learning to preserve the spatial structure in the lower manifold and mitigate boundary distortion. Thirdly, the occlusion estimating strategy, performing on-line evaluation of tracking, triggers the motion estimation module to optimize the optimal location. Experiments on benchmark databases demonstrate the enhanced discrimination and interpretability of the proposed tracker and its superiority over other trackers.