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Siamese trackers perform similarity matching with templates (i.e., targetmodels) to recursively localize objects within a search region. Severalstrategies have been proposed in the literature to update a template based onthe tracker output, typically extracted from the target search region in thecurrent frame, and thereby mitigate the effects of target drift. However, thismay lead to corrupted templates, limiting the potential benefits of a templateupdate strategy.  This paper proposes a model adaptation method for Siamese trackers that usesa generative model to produce a synthetic template from the object searchregions of several previous frames, rather than directly using the trackeroutput. Since the search region encompasses the target, attention from thesearch region is used for robust model adaptation. In particular, our approachrelies on an auto-encoder trained through adversarial learning to detectchanges in a target object's appearance and predict a future target template,using a set of target templates localized from tracker outputs at previousframes. To prevent template corruption during the update, the proposed trackeralso performs change detection using the generative model to suspend updatesuntil the tracker stabilizes, and robust matching can resume through dynamictemplate fusion.  Extensive experiments conducted on VOT-16, VOT-17, OTB-50, and OTB-100datasets highlight the effectiveness of our method, along with the impact ofits key components. Results indicate that our proposed approach can outperformstate-of-art trackers, and its overall robustness allows tracking for a longertime before failure.

Generative Target Update for Adaptive Siamese Tracking