Channel-graph regularized correlation filter for visual object tracking

Abstract

Correlation filter (CF) based tracker often disregard or weakly incorporate the importance of feature channels as well as channel similarity. To address this, we propose a channel-graph regularization correlation filter-based visual object tracker (CGRCF). In our work, we study two-channel regularization methods. First is the channel regularization that determines the vital feature channels. Second is the graph-regularization that increases the probability of assigning similar weights based on the properties of feature channels. The proposed tracker can be efficiently solved in the Fourier domain using ADMM (Alternate Direction Method of Multiplier) and achieves a real-time tracking speed of 28FPS. We conduct extensive experimentation on the TC128, VOT2017 and VOT2019 datasets. The proposed tracker demonstrates promising results and performs better than several state of the art CF trackers as well as end-to-end deep learning trackers.