Visual Perception based Adaptive Feature Fusion for Visual Object Tracking

To overcome visual object tracking challenges, various feature-based object trackers use feature combination. Each feature component is developed to overcome certain tracking challenges, but the interaction between the components may cause tracking errors. We propose a tracking solution based on human vision principles to reduce combination errors by adaptively fusing each feature using its previous performance. An adaptive fusion technique is developed to determine feature quality using feature likelihood map variance ratios. The proposed method is completely modular, while reducing the risk of tracker failure. Experimental results on the Visual Object Tracking database show the proposed tracker's robustness and its advantage over state-of-the-art trackers.