Visual Tracking via Weakly Supervised Learning from Multiple Imperfect Oracles

Long-term persistent tracking in ever-changing environments is a challenging task, which often requires addressing difficult object appearance update problems. To solve them, most top-performing methods rely on online learning-based algorithms. Unfortunately, one inherent problem of online learning-based trackers is drift, a gradual adaptation of the tracker to non-targets. To alleviate this problem, we consider visual tracking in a novel weakly supervised learning scenario where (possibly noisy) labels but no ground truth are provided by multiple imperfect oracles (i.e., trackers), some of which may be mediocre. A probabilistic approach is proposed to simultaneously infer the most likely object position and the accuracy of each tracker. Moreover, an online evaluation strategy of trackers and a heuristic training data selection scheme are adopted to make the inference more effective and fast. Consequently, the proposed method can avoid the pitfalls of purely single tracking approaches...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Bineng Zhong, Hongxun Yao, Sheng Chen, Xiaotong Yuan, Shaohui Liu
• Keywords: Computer Vision, CVPR 2010, Long-term Persistent Tracking, Object Appearance Update, Online Learning-based Trackers