Video Object Tracking Based on Residual Dense Siamese Network

To solve the problem that the existing video object tracking (VOT) methods based on siamese networks have poor tracking results due to the lack of feature extraction ability, such as severe object appearance change and out-of-plane rotation, a VOT method based on residual dense siamese networks was proposed. First, the residual dense network embedded convolutional block attention module was designed to extract features at different levels from the template image and the detection image. Then, the features of different levels were interlinked by an independent region proposal network. Finally, the outputs of multiple region proposal networks were summed up adaptively and the final tracking result was obtained. Results show that the proposed method can achieve better tracking effect when dealing with challenges such as severe object appearance change and out-of-plane rotation.