Target-Aware Correlation Filter Tracking in RGBD Videos

Abstract

Recently, the surge of depth sensors makes RGBD data available and facilitates the development of RGBD tracking. Inspired by the popularity of discriminative correlation filter (DCF)-based trackers in RGB tracking, we propose a target-aware framework for RGBD tracking based on the existing DCF-based trackers in this paper. The framework comprehensively utilizes the prior, depth, and color information to compute a fine foreground-background segmentation map. This map is masked on the extracted features for adaptive reweighting and adjusts the DCF-based trackers to focus on the area with higher target possibility. Meanwhile, we propose an occlusion detection and handling mechanism based on this segmentation map, which helps to detect an occlusion early and avoids contaminations of the target model. Our proposed framework overcomes the limitations of the cosine window generally adopted in the existing DCF-based trackers and could be incorporated into these trackers. Experiments on the princeton tracking benchmark (PTB) demonstrate that DCF-based trackers outperform their baselines after integrated with our framework. Among these enhanced trackers, our proposed tracker ECO_TA (target-aware) based on efficient convolution operators (ECOs) for tracking achieves the best performance on the PTB and another RGBD benchmarks.