Robust object tracking via active feature selection

Adaptive tracking by detection has been widely studied with promising results. The key idea of such trackers is how to train an online discriminative classifier, which can well separate an object from its local background. The classifier is incrementally updated using positive and negative samples extracted from the current frame around the detected object location. However, if the detection is less accurate, the samples are likely to be less accurately extracted, thereby leading to visual drift. Recently, the multiple instance learning (MIL) based tracker has been proposed to solve these problems to some degree. It puts samples into the positive and negative bags, and then selects some features with an online boosting method via maximizing the bag likelihood function. Finally, the selected features are combined for classification. However, in MIL tracker the features are selected by a likelihood function, which can be less informative to tell the target from complex background. Motivated by the active learning method, in this paper we propose an active feature selection approach that is able to select more informative features than the MIL tracker by using the Fisher information criterion to measure the uncertainty of the classification model. More specifically, we propose an online boosting feature selection approach via optimizing the Fisher information criterion, which can yield more robust and efficient real-time object tracking performance. Experimental evaluations on challenging sequences demonstrate the efficiency, accuracy, and robustness of the proposed tracker in comparison with state-of-the-art trackers.