Correlation filters (CF)-based tracking methods have drawn extensive attention due to their impressive performance in visual object tracking (VOT) challenges. However, the existing CF-based trackers incur the inherent problems of boundary effect and filter degradation. Meanwhile, the scale variants of targets result in tracking-drift problems, which degrade the tracking accuracy significantly. To address the aforementioned problems, a tracking model named spatiotemporal regularization and scale-aware correlation filters (STR-SACF) is proposed. The STR-SACF model consists of two CFs, namely translation filter and scale filter (SF). The translation filter improves the accuracy of target localization, whereas the SF achieves the optimal and fast scale estimation. The proposed tracking model can improve the tracking accuracy while reducing computational complexity. Moreover, a spatiotemporal regularization term is introduced into the translation filter and SF to suppress boundary effect and filter degradation. The proposed model is effectively optimized based on the alternating direction method of multipliers algorithm. Experimental results on four challenging tracking benchmarks, i.e., OTB2013, OTB2015, TC128, and VOT2016, demonstrate the superiority of the proposed tracker compared with more than 20 state-of-the-art trackers.