Real-Time Affine Tracking Using Re-located Lucas-Kanade Algorithm

Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers are lack of an ability to adapt affine transforms of objects. The Lucas-Kanade(L-K) algorithm is a classic gradient decent search method which has been widely applied in template matching, image alignment and visual tracking. Despite the L-K algorithm has a good property on adapting targets affine transforms, it always falls into a local optimal since the background information is accumulatively introduced to the template. In this paper, we propose a novel approach, called the Re-located L-K algorithm(RL-K), to handle affine transforms of objects. In particular, we utilize the Multiple Channel Correlation Filters(MMCFs) to deal with the translations of the object before the L-K´s gradient decent search step. In addition, we integrate the gradient orientation features into the L-K algorithm to improve its robustness to visual tracking. The experimental results show that, comparing with the state-of-the-art affine tracking approaches, our approach is running at real-time without accuracy losing.