Tracking Algorithm for Siamese Network Based on Target-Aware Feature Selection

Tracking algorithms implemented in Siamese networks utilize an offline training network to extract features from a target object for matching and tracking. The offline-trained deep features arc less efficient for distinguishing targets with arbitrary forms from the background. Therefore, we proposed a tracking algorithm for a Siamese network based on target-aware feature selection. First, the cropped template and detection frames were sent to a feature extraction network based on ResNet50 to extract the shallow, middle and deep features of the target and search regions. Second, in the target-aware module, a regression loss function was formulated for target-aware features and an importance scale for each convolution kernel was obtained based on backpropagated gradients. Then, the convolution kernels with large importance scales were activated to select target-aware features. Finally, the selected features were inputted into the SiamRPN module for target-background classification and the bounding box regression was applied to obtain an accurate bounding box of the target. Results of experiments on OTB2015 and VOT2018 datasets confirm that the proposed algorithm can achieve robust tracking of the target.