Visual object tracking using sparse context-aware spatio-temporal correlation filter

This paper presents a novel sparse context-aware spatio-temporal correlation filter tracker (SCAST) method for robust visual object tracking. Different from the existing trackers, this paper introduce an l(1) multi-scale regularization parameter-based correlation filter that reduces the boundary effect due to partial occlusions, illumination and scale variations. At each iteration, the l(1) regularization parameter is updated through spatial knowledge of each correlation filter coefficient. Besides, the contextual information acquired from the target region can lead to determining the accurate localization of the target. Moreover, contextual information has combined with spatio-temporal factor to achieve the better performance. Further, an objective function is designed with system constraints to ensure the applicability of the model and the optimal solution is derived by utilizing the alternating direction method of multiplier, which leads to low computational cost. Finally, the feasibility and superiority of proposed tracker algorithm is evaluated through three benchmark dataset: OTB-2013, OTB-2015, and TempleColor-128. (C) 2020 Elsevier Inc. All rights reserved.