Adaptive Randomized Ensemble Tracking Using Appearance Variation and Occlusion Estimation

Tracking-by-detection methods have been widely studied with promising results. These methods usually train a classifier or a pool of classifiers in an online manner and use previous tracking results to generate a new training set for object appearance and update the current model to predict the object location in subsequent frames. However, the updating process may easily cause drifting in terms of appearance variation and occlusion. The previous methods for updating the classifier(s) decided whether or not to update the classifier(s) by a fixed learning rate parameter in all scenarios. The learning rate parameter has a great influence on the tracker’s performance and should be dynamically adjusted according to the change of scene during tracking. In this paper, we propose a novel method to model the time-varying appearance of an object that takes appearance variation and occlusion of local patches into consideration. In contrast with the existing methods, the learning rate for updating classifier ensembles adaptively is adjusted by estimating the appearance variation with sparse optical flow and the possible occlusion of the object between consecutive frames. Experiments and evaluations on some challenging video sequences have been done and the results demonstrate that the proposed method is more robust against appearance variation and occlusion than those state-of-the-art approaches.

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