Kernel-based object tracking using particle filter with incremental Bhattacharyya similarity

Abstract

In this paper, we propose a method for kernel-based object tracking in order to deal with partial occlusion. We use particle filter to estimate target position accurately. The incremental Bhattacharyya Dissimilarity (IBD) based stage is designed to consistently distinguish the particles located in the object region from the others placed in the background. 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.