Orientation and Scale Invariant Kernel-Based Object Tracking with Probabilistic Emphasizing

Kwang Moo Yi, Soo Wan Kim, Jin Young Choi

Abstract: Tracking object with complex movements and background clutter is a challenging problem. The widely used mean-shift algorithm shows unsatisfactory results in such situations. To solve this problem, we propose a new mean-shift based tracking algorithm. Our method is consisted of three parts. First, a new objective function for mean-shift is proposed to handle background clutter problems. Second, orientation estimation method is proposed to extend the dimension of trackable movements. Third, a method using a new scale descriptor is proposed to adapt to scale changes of the object. To demonstrate the effectiveness of our method, we tested with several image sequences. Our algorithm is shown to be robust to background clutter and is able to track complex movements very accurately even in shaky scenarios.