Object tracking via Spatio-Temporal Context learning based on multi-feature fusion in stationary scene

A robust algorithm is proposed for tracking object in dynamic challenges including illumination change, pose variation, and occlusion in stationary scene. To cope with these factors, the Spatio-Temporal Context learning based on Multi-feature (MSTC) is integrated within a fusion framework. Different from the original Spatio-Temporal Context learning (STC) algorithm which exploits the low-level features (i.e. image intensity and position) from the target and its surrounding regions, our approach utilize the high-level features like Histogram of Oriented Gradient (HOG) and low-level features for tracker interaction and selection for robust tracking performance in decision level. Experimental results on benchmark datasets demonstrate that the proposed algorithm performs robustly and favorably against the original algorithm.