Discriminative Nonorthogonal Binary Subspace Tracking

Abstract

Visual tracking is one of the central problems in computer vision. A crucial problem of tracking is how to represent the object. Traditional appearance-based trackers are using increasingly more complex features in order to be robust. However, complex representations typically will not only require more computation for feature extraction, but also make the state inference complicated. In this paper, we show that with a careful feature selection scheme, extremely simple yet discriminative features can be used for robust object tracking. The central component of the proposed method is a succinct and discriminative representation of image template using discriminative non-orthogonal binary subspace spanned by Haar-like features. These Haar-like bases are selected from the over-complete dictionary using a variation of the OOMP (optimized orthogonal matching pursuit). Such a representation inherits the merits of original NBS in that it can be used to efficiently describe the object. It also incorporates the discriminative information to distinguish the foreground and background. We apply the discriminative NBS to object tracking through SSD-based template matching. An update scheme of the discriminative NBS is devised in order to accommodate object appearance changes. We validate the effectiveness of our method through extensive experiments on challenging videos and demonstrate its capability to track objects in clutter and moving background.