Sports Target Tracking Based on Discriminant Correlation Filter and Convolutional Residual Network

During the sports tracking process, a moving target often encounters sophisticated scenarios such as fast motion and occlusion. During this period, erroneous tracking information will be generated and delivered to the next frame for updating; the information will seriously deteriorate the overall tracking model. To address the problem mentioned above, in this paper, we propose a convolution residual network model based on a discriminative correlation filter. The proposed tracking method uses discriminative correlation filters as basic convolutional layers in convolutional neural networks and then integrates feature extraction, response graph generation, and model updates into end-to-end convolutional neural networks for model training and prediction. Meanwhile, the introduction of residual learning responds to the model failure due to changes in the target appearance during the tracking process. Finally, multiple features are integrated such as HOG (histogram of oriented gradient), CN (color names), and histogram of local intensities for comprehensive feature representation, which further improve the tracking performance. We evaluate the performance of the proposed tracker on MultiSports datasets; the experimental results demonstrate that the proposed tracker performs favorably against most state-of-the-art discriminative correlation filter-based trackers, and the effectiveness of the feature extraction of the convolutional residual network is verified.