Concerning the problem that Context-Ware Correlation Filter (CACF) algorithm based on artificial features has poor tracking performance under the situations of deformation, motion blur and low resolution and when the tracker encounters conditions like severe occlusion, it is easy to fall into local optimum and cause tracking failure, a new object tracking algorithm combining re-detection mechanism and Convolutional Regression Network (CRN) was proposed. In the training phase, the correlation filter was integrated into the deep neural network as a CRN layer, so that the network became a whole for end-to-end training. In the tracking phase, different network layers and their response values were merged through residual connections. At the same time, a re-detection mechanism was introduced to make the tracking algorithm recover from the potential tracking failure, and the re-detector would be activated when the response value was lower than the given threshold. Experimental results on the dataset OTB-2013 show that the proposed algorithm achieves 88.1% accuracy on 50 video sequences, which is 9.7 percentage points higher than the accuracy of original CACF algorithm, and has better results compared with original algorithm on video sequences with attributes like deformation and motion blur.