In order to solve the problem of tracking failure of the Sum of template and pixel-wise learners (Staple) tracking algorithm for in-plane rotation and partial occlusion, a simple and effective Channel Stability-weighted Staple (CSStaple) tracking algorithm was proposed.Firstly, a standard correlation filter classifier was employed to detect the response value of each channel. Then, the stability weight of each channel was calculated and multiplied to the weight of each layer to obtain correlation filtering response. Finally, by integrating the response of the color complementary learner, the final response result was obtained, and the location of the maximum value in the response was the tracking result. The proposed algorithm was compared with several state-of-the-art tracking algorithms including Channel and Spatial Reliability Discriminative Correlation Filter (CSR-DCF) tracking, Hedged Deep Tracking (HDT), Kernelized Correlation Filter (KCF) Tracking and Staple. The experimental results show that, the proposed algorithm performs best in the success rate, it is 2.5 percentage points higher and 0.9 percentage points higher than Staple on OTB50 and OTB100 respectively, which proves the effectiveness of the proposed algorithm for target in-plane rotation and partial occlusion.