Learning regression and verification networks for long-term visual tracking
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In the long-term single object tracking task, the target moves out of view frequently. It is difficult to determine the presence of the target and re-search the target in the entire image. In this paper, we circumvent this issue by introducing a collaborative framework that exploits both matching mechanism and discriminative features to account for target identification and image-wide re-detection. Within the proposed collaborative framework, we develop a matching based regression module and a classification based verification module for long-term visual tracking. In the regression module, we present a regressor that conducts matching learning and copes with drastic appearance changes. In the verification module, we propose a classifier that filters out distractions efficiently. Compared to previous long-term trackers, the proposed tracker is able to track the target object more robustly in long-term sequences. Extensive experiments show that our algorithm achieves state-of-the-art results on several datasets.
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