Multi-scale kernel correlation filter for visual tracking

Correlation filter, previously used in object detection and recognition assignment within single image, has become a popular approach to visual tracking due to its high efficiency and robustness. Many trackers based on the correlation filter, including Minimum Output Sum of Squared Error (MOSSE), Circulant Structure tracker with Kernels (CSK) and Kernel Correlation Filter (KCF), they simply estimate the translation of a target and provide no insight into the scale variation of a target. But in visual tracking, scale variation is one of the most common challenges and it influences the visual tracking performance in stability and accuracy. Thus, it is necessary to handle the scale variation. In this paper, we present an accurate scale estimation solution with two steps based on the KCF framework in order to tackle the changing of target scale. Meanwhile, besides the original pixel grayscale feature, we integrate the powerful features Histogram of Gradient (HoG) and Color Names (CN) together to further boost the overall visual tracking performance. Finally, the experimental results demonstrate that the proposed method outperforms other state-of-the-art trackers.