For current correlation filter target tracking algorithm, the spatial regularization weight is not connected with an object, and the temporal regularization term fails to update adaptively. To resolve this problem, a correlation filter based on adaptive spatiotemporal regularization was proposed. The adaptive spatial regularization term first obtains the spatial regularization weight connected with the object by initial-frame saliency aware reference weight. Second, the reference value of the temporal regularization parameter is calculated using the altered response score between two adjacent frames. Thus, the adaptive temporal regularization term can be continuously updated by the changing regularization parameter. Finally, the algorithm is optimized by the alternating direction method of multipliers, which reduces the number of iterations and solves the related parameters (filtering function, spatial regularization weight, and temporal regularization parameter). In an experimental evaluation on OTB-2015 dataset, our algorithm outperformed comparable algorithms, achieving a distance precision of 86.4% and a success rate of 65.6%. The proposed algorithm also showed higher robustness in complex scenes with deformation, rotation, occlusions, and out of view than the competing algorithms.