Motion Correlation Discovery for Visual Tracking

Motion information plays an important role in identifying moving objects, which has not been well utilized in state-of-the-art tracking algorithms. In this letter, we propose a unified framework integrating two tracking problems, i.e., pixel-level foreground probabilistic inference and motion parameter estimation. Our model employs motion fields to propagate probability forward, and discovers motion patterns in the spatial domain to distinguish targets from the background. It takes advantage of continuity and inertia of both target and camera motion, and provides reliable evidence to resolve confusion caused by appearance similarity between targets and the background. Target localization is effectively achieved from the pixel-level foreground probabilistic map. Experimental results demonstrate that the proposed method significantly improves our baseline method, and achieves performance comparable to state-of-the-art tracking methods with more complex features.