Bag of Features Tracking

In this paper, we propose a visual tracking approach based on "bag of features" (BoF) algorithm. We randomly sample image patches within the object region in training frames for constructing two codebooks using RGB and LBP features, instead of only one codebook in traditional BoF. Tracking is accomplished by searching for the highest similarity between candidates and codebooks. Besides, updating mechanism and result refinement scheme are included in BoF tracking. We fuse patch-based approach and global template-based approach into a unified framework. Experiments demonstrate that our approach is robust in handling occlusion, scaling and rotation.