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The performance of an adaptive tracking-by-detection algorithm not onlydepends on the classification and updating processes but also on the sampling.Typically, such trackers select their samples from the vicinity of the lastpredicted object location, or from its expected location using a pre-definedmotion model, which does not exploit the contents of the samples nor theinformation provided by the classifier. We introduced the idea of mostinformative sampling, in which the sampler attempts to select samples thattrouble the classifier of a discriminative tracker. We then proposed an activediscriminative co-tracker that embed an adversarial sampler to increase itsrobustness against various tracking challenges. Experiments show that ourproposed tracker outperforms state-of-the-art trackers on various benchmarkvideos.

Information-Maximizing Sampling to Promote Tracking-by-Detection