Discriminative Nonorthogonal Binary Subspace Tracking

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...

• Type: Journal
• Year: 2010
• Where: ECCV
• Authors: Ang Li, Feng Tang, Yanwen Guo, Hai Tao
• Keywords: Computer Vision, Discriminative Nbs, ECCV 2010, Object Tracking, Robust Object Tracking