Robust Visual Tracking With Occlusion Judgment and Re-Detection

Abstract

A detection algorithm is often used to remedy tracking failures in a typical single-target visual tracking algorithm. In practice, when a target is occluded for a long time, neither the tracking module nor the detection module can accurately predict the position of the target. To accurately locate the target, we first introduce the l1l2 loss function to reduce the sensitivity of correlation filter-based method to local occlusion. To solve the instability of algorithms based on the single feature in complex scene, we use the histogram of oriented gradient (HOG) features and color names (CN) features to train a filter respectively, and the fusion weights are calculated according to the difference between the response value of each filter and the expected response value. At the same time, we adaptively update the model online by calculating the sensitivity of different filters. We follow the re-detection idea in long-term tracking, the peak to sidelobe ratio (PSR) is used to judge the serious occlusion, and we use support vector machine (SVM) for re-detection after severe occlusion or target out-of-view. In this paper, 34 sets of sequences are selected to evaluate the proposed algorithm. The sufficient experimental results demonstrate that our algorithm has strong anti-occlusion ability and robustness performance. We compare our proposal with several state-of-the-art algorithms under all the sequences of OTB100, and our algorithm yields highly competitive performance for tracking.

Keywords

• Correlation filter
• re-detection
• support vector machine
• visual tracking