Robust Visual Tracking via Adaptive Occlusion Detection

Abstract

Occlusion is a special challenge in visual tracking, which may cause target template corrupted by background information. In this paper, we propose an adaptive occlusion detection framework for robust tracking against occlusion. The framework consists of a patch tracker, an occlusion detector, a template updater and a search window predictor. The patch tracker applies KCF-based method to track background patch individually, which may occlude target. The occlusion detector searches for background patches occluding target with an adaptive threshold. The template updater evaluates the occlusion state and applies appropriate target template update strategy. The search window predictor adaptively rescales the size of search window based on occlusion state. Experiments in OTB50 demonstrate that our tracker achieves comparable performance compared with other state-of-art trackers and outperforms them in cases of occlusion.