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MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization*Jianming Zhang, Shugao Ma, and Stan Sclaroff Department of Computer Science, Boston University, USAjmzhang@bu.edu shugaoma@bu.edu sclaroff@bu.edu Abstract. We propose a multi-expert restoration scheme to address the model drift problem in online tracking. In the proposed scheme, a tracker and its historical snapshots constitute an expert ensemble, where the best expert is selected to restore the current tracker when needed based on a minimum entropy criterion, so as to correct undesirable model updates. The base tracker in our formulation exploits an online SVM on a budget algorithm and an explicit feature mapping method for efficient model update and inference. In experiments, our tracking method achieves substantially better overall performance than 32 trackers on a benchmark dataset of 50 video sequences under various evaluation settings. In addition, in experiments with a newly collected dataset of challenging sequences, we show that the proposed multi-expert restoration scheme significantly improves the robustness of our base tracker, especially in scenarios with frequent occlusions and repetitive appearance variations. Electronic Supplementary Material: LNCS 8694, p. 188 ff. lncs@springer.com
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