Discriminative Correlation Filters (DCFs) based trackers are subject to interference from background noises surrounding the target, which makes the DCF training easy to learn the filter from the contextual environment. To resolve this, this paper proposes a novel Contextual Disturbance-aware Correlation Filter (CDCF) target tracking algorithm, which establishes a background contextual disturbance model to guide the decision-making process of the tracker in the current frame. Firstly, CDCF employs the latest contextual background patches to generate a large number of negative samples through spatial cropping and correlation operations, followed by implementing them as a regularization term into the objective function. Secondly, CDCF incorporates an aberration repressed regularization term, which avoids the response map aberration problem caused by deformation, occlusion, etc., via the reuse of the filter knowledge learnt from the historical frames. Finally, an optimization method based on Alternating Direction Method of Multipliers (ADMM) is designed to achieve the implementation of the cropping operation in the spatial domain and the high-speed solution of the filter in the frequency domain. Quantitative and qualitative evaluations on OTB-2013 and OTB-2015 datasets demonstrate the effectiveness of the proposed algorithm.