A unique target representation and voting mechanism for visual tracking

One of the main problems in visual tracking is how to robustly represent the target. In this paper, we propose a simple yet efficient method that provides a unique “target representation” by generating discriminative non-uniform subspaces from the feature space which we refer to as cells. Each cell is attributed with a measure that highlights how likely it describes the target or the background. In addition, we keep a codebook of spatial locations of the features which are mapped to the cell similar to that of the R-Table in generalized Hough transform. Using the uniqueness measure as weight, the target center is estimated by using a modified Hough voting scheme to address non-rigid deformations. In the experiment, we use color as the pixel's descriptor and demonstrate comparable performance on the Online Tracking Benchmark (OTB) dataset respect to the other state-of-the-art.