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This paper improves state-of-the-art visual object trackers that use onlineadaptation. Our core contribution is an offline meta-learning-based method toadjust the initial deep networks used in online adaptation-based tracking. Themeta learning is driven by the goal of deep networks that can quickly beadapted to robustly model a particular target in future frames. Ideally theresulting models focus on features that are useful for future frames, and avoidoverfitting to background clutter, small parts of the target, or noise. Byenforcing a small number of update iterations during meta-learning, theresulting networks train significantly faster. We demonstrate this approach ontop of the high performance tracking approaches: tracking-by-detection basedMDNet and the correlation based CREST. Experimental results on standardbenchmarks, OTB2015 and VOT2016, show that our meta-learned versions of bothtrackers improve speed, accuracy, and robustness.

Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers