Abstract: In order to improve the accuracy of the target tracking and achieve real-time tracking, we proposed a new method based on improved siamese fully convolutional networks: siamese squeeze and excitation fully convolutional networks (Siam-SEFC). Siam-SEFC fused spatial channel information by adding squeeze and excitation networks structure with a small number of parameters, added spatial information for the tracking object, and adjusted the training data scale for variable-scale data enhancement to extract multi-scale features, which effectively improved the accuracy of target tracking. Compared with the three algorithms of MDNet, SENet and DAT, Siam-SEFC has real-time performance while ensuring the accuracy of target tracking. Compared with Siamese-FC, the tracking accuracy of Siam-SEFC is improved by 2.2%, the number of parameters is only increased by 1.01%, and there is no loss of real-time performance, which verifies the effectiveness of the improved scheme.

Key words: target tracking, siamese fully convolutional networks, real-time, multi-scale feature, data enhancement