Adaptive Kernel-Based Object Tracking with Robust Appereance Model Using Particle Filter

In this paper we propose a method to real time kernel-based human tracking for dealing with partial occlusion. While the target is occluded by background or other objects, the kernel parameters change which adaptively improves the target model. In addition, the number of particles increases in the next frame. To attain the appropriate accuracy in tracking, we use multi-feature to describe the target. The color histogram feature is robust to scale, orientation, partial occlusion and non-rigidity of the object. However, this feature is sensitive to illumination variations. Therefore, we utilize the combination of color histogram and generalized LBP for object edge points to describe an appropriate target model. The performance of this method is evaluated for real world scenarios such as PETS benchmark in which the target is occluded by the background or other objects.