A Highly Efficient and Robust Method for NNF-Based Template Matching

Template matching is widely used in many applications such as arbitrary object detection and tracking. In this paper, we propose a highly efficient and robust method for template matching. The state-of-the-art nearest neighbor field (NNF)-based algorithms construct the NNF by searching the nearest neighbor of the target points. Instead, our method utilize the inverse NNF by searching the nearest neighbor of template points, which is more robust than original NNF. As for similarity metric, our method utilizes the diversity on the inverse NNF to calculate a global probability image and makes use of the integral image to calculate the similarity score, which makes our method highly efficient. Furthermore, our method is a non-parametric model and has non-explicit assumptions about data. Experiments on challenging public datasets demonstrate the superiority of our method to state-of-the-art methods both in robustness and efficiency.