Most tracking-by-detection algorithms adopt an online learning classifier to separate targets from their surrounding background. These methods set a sliding window to extract some candidate samples from the local regions surrounding the former object location at current frame. The trained classifier is then applied to these samples, which sample with the maximum classifier score is considered as the new object location. However, in classifier training procedure, noisy samples may often be included when they are not
enough, thereby causing visual drift. Online discriminative feature selection (ODFS) method has been recently introduced into the tracking algorithms, which can alleviate drift to some extent. However, the ODFS tracker may detect the candidate sample that is less accurate because it does not discriminatively take the sample importance into consideration during the feature selection procedure. In this paper, we present a novel weighted online discriminative feature selection (WODFS) tracker, which integrates the sample’s contribution into the optimization procedure when selecting features, the proposed method optimizes the objective function in the steepest ascent direction with respect to the weighted positive samples while in the steepest descent direction with respect to the negative. Therefore, the selected features directly couple their scores with the contribution of samples which result in a more robust and stable tracker. Numerous experiments on challenging sequences demonstrate the superiority of the proposed algorithm.