Structural local DCT sparse appearance model for visual tracking

The success of sparse representation in face recognition has motivated the development of sparse representation-based appearance models for visual tracking. These sparse representation-based trackers show state-of-the-art performance, but at the cost of computationally expensive l₁-norm minimization. As the computational cost prevents the tracker from being used in real-time systems such as real-time surveillance and military operations, it has become a very important issue. With the aim of reducing the computational complexity of l₁-norm minimization, a structural local DCT sparse appearance model is proposed in a particle filter framework. Application of DCT on local patches helps to reduce the dimensions of the dictionary as well as candidate samples by using low-pass filtered DCT coefficients. This in turn helps to remove the information relating to occlusion and background clutter thereby reducing the ambiguity created while computing the confidences of the target samples. The proposed method is evaluated on the challenging image sequences available in the literature and its performance compared with three recent state-of-the-art methods. It is shown that the proposed method provides superior/similar performance for most of the sequences with reduced computational complexity in l₁-norm minimization.