Fast Generative Approach Based on Sparse Representation for Visual Tracking

One of issue in generative approach for visual tracking is relates to computation time. It is because generative approach uses particle filter for modeling the motion and as a method to predict the state in the current frame. The system will be more accurate but slower computation if many particles are used. Recently, the combination between particle filter and sparse model is proposed to handle appearance variations and occlusion in visual tracking. Unfortunately, the issue about computation time still remains. This paper presents fast method for sparse generative approach in visual tracking. In this method, 11 minimization is used to calculate sparse coefficient vector for each candidate sample. Then, the maximum weighted is selected to represent the result. Based on simulations, our proposed method demonstrate good result in area under curve parameter and achieve four times faster than other methods with only use fifty particles.