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The presence of objects that are confusingly similar to the tracked target,poses a fundamental challenge in appearance-based visual tracking. Suchdistractor objects are easily misclassified as the target itself, leading toeventual tracking failure. While most methods strive to suppress distractorsthrough more powerful appearance models, we take an alternative approach.  We propose to keep track of distractor objects in order to continue trackingthe target. To this end, we introduce a learned association network, allowingus to propagate the identities of all target candidates from frame-to-frame. Totackle the problem of lacking ground-truth correspondences between distractorobjects in visual tracking, we propose a training strategy that combinespartial annotations with self-supervision. We conduct comprehensiveexperimental validation and analysis of our approach on several challengingdatasets. Our tracker sets a new state-of-the-art on six benchmarks, achievingan AUC score of 67.1% on LaSOT and a +5.8% absolute gain on the OxUvA long-termdataset.

Learning Target Candidate Association to Keep Track of What Not to Track