Herein, a context-aware correlation filter tracking algorithm based on the Gaussian output constraint (OCCACF) is proposed to reduce the occurrence of drift in the target tracking process. This algorithm assumes that the output response of the tracking target obeys Gaussian distribution. A form of constraint output is derived from the properties of Gaussian distribution and an iterative parameter is obtained using the constraint output and correlation filter knowledge. The filters in this tracker are selectively updated according to setting constraints. The effectiveness of the proposed algorithm is verified using 50 video sequences in the OTB-2013 evaluation benchmark and the proposed algorithm is compared with other tracking algorithms. Experimental results show that the proposed algorithm can significantly improve the overall performance of target tracking and has obvious advantages than other algorithms that have been proposed in recent years.