Enhanced Distribution Field Tracking Using Channel Representations

Visual tracking of objects under varying lighting conditions and changes of the object appearance, such as articulation and change of aspect, is a challenging problem. Due to its robustness and speed, distribution field tracking is among the state-of-the-art approaches for tracking objects with constant size in grayscale sequences. According to the theory of averaged shifted histograms, distribution fields are an approximation of kernel density estimates. Another, more efficient approximation are channel representations, which are used in the present paper to derive an enhanced computational scheme for tracking. This enhanced distribution field tracking method outperforms several state-of-the-art methods on the VOT2013 challenge, which evaluates accuracy, robustness, and speed.