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 introduces an l1 multi-scale regularization parameter-based correlation filter that reduces the boundary effect due to partial occlusions, illumination, and scale variations. At each iteration, the l1 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 been combined with spatio-temporal factors to achieve 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 the proposed tracker algorithm is evaluated through three benchmark dataset: OTB-2013, OTB-2015, and TempleColor-128.