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Erschienen in:
2014 | OriginalPaper | Buchkapitel
MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization
verfasst von : Jianming Zhang, Shugao Ma, Stan Sclaroff
Erschienen in: Computer Vision – ECCV 2014
Verlag: Springer International Publishing
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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.