Robust visual tracking using joint scale-spatial correlation filters

Abstract

Scale adaptation is crucial to object tracking as the visual size of the target changes continuously. Many existing tracking algorithms, however, simply ignore scale changes either for the consideration of tracking efficiency or the lack of principle ways to scale estimation. In this work, we present an efficient and effective scale adaptive tracking algorithm by proposing a correlation filter based tracker in the joint spatial and scale space. We find that the exhaustive template searching in this joint space can be well modeled by a block-circulant matrix. With the properties of the block-circulant matrices, we prove that the expensive template matching can be transformed to efficient dot product in frequency domain by fast Fourier Transform. Based on these findings, our new tracker significantly improves the robustness and adaptability of previous competitive spatial correlation trackers. On the latest single object tracking benchmark, our tracker advances the state-of-the-art tracking results with a very large margin.