Visual Tracking via Reliable Memories
This addresses drift issues in visual tracking for applications like surveillance or robotics, but appears incremental as it builds on existing tracking methods with novel components.
The paper tackles the problem of drift error in long-term visual tracking by proposing a framework that discovers reliable patterns from video to resist drift, achieving robust tracking over 4000 frames while others fail early.
In this paper, we propose a novel visual tracking framework that intelligently discovers reliable patterns from a wide range of video to resist drift error for long-term tracking tasks. First, we design a Discrete Fourier Transform (DFT) based tracker which is able to exploit a large number of tracked samples while still ensures real-time performance. Second, we propose a clustering method with temporal constraints to explore and memorize consistent patterns from previous frames, named as reliable memories. By virtue of this method, our tracker can utilize uncontaminated information to alleviate drifting issues. Experimental results show that our tracker performs favorably against other state of-the-art methods on benchmark datasets. Furthermore, it is significantly competent in handling drifts and able to robustly track challenging long videos over 4000 frames, while most of others lose track at early frames.