Making Bayesian tracking and matching by the BRISK interest points detector/descriptor cooperate for robust object tracking

摘要

Visual Tracking is often hindered by difficulties such as occlusion, abrupt motion and out of view problems. Motivated by the wide range of existing features, search mechanisms and target representations, a cooperation strategy between Bayesian tracking and matching by the Binary Robust Invariant Scalable Key-points (BRISK) is devised in this paper. The Bayesian tracker is considered as the main tracker, while matching by the BRISK is used for lost target recovery. Switching to matching by the BRISK is based on the spatial uncertainty of the particles, whenever the spatial uncertainty is above a threshold indicating a tracking failure, matching by the BRISK is trigged on and executed until the target is recovered. When the target is re-detected, the tracking control is given back to the color based particle filter tracker (PF). Experiment results show the effectiveness of the proposed tracking framework.