关键词: 目标跟踪, 孪生网络, 参考特征图融合, 深度互相关, 候选窗口响应（RoWs）, 权衡锚点, 漂移采样, 训练加速

Abstract:

There are problems in object tracking based on siamese networks, such as insufficient feature information, tracking efficiency needing to be improved, and long training time on large datasets. In order to solve the above problems, efficient object tracking based on feature fusion and training acceleration is proposed. The reference feature level of backbone network is increased, down-sampling is reduced, and multi-level reference feature maps are fused with the purpose of extracting deeper and more abundant semantic targets information. Depth-wise cross correlation is used to get RoWs （Response of Candidate Windows）, in which the Region Proposal Network（RPN） is constructed. By balancing the number of positive and negative anchors, the performance of siamese networks is more efficient and steady. When training siamese networks on large datasets, uniformly sliding shift sampling is used instead of randomly shift sampling, which significantly accelerates training speed at the time of suppressing center bias phenomenon. Experimental evaluation results on tracking benchmark VOT2018 show that the proposed algorithm has the best tracking performance compared with all reference mainstream object tracking algorithms.

Key words: object tracking, siamese network, reference feature map fusion, depth-wise cross correlation, Response of Candidate Windows（RoWs）, balancing anchors, shift sampling, training acceleration