关键词: 孪生网络, 深度残差网络, 注意力机制, 无人机, 目标跟踪

Abstract: Object tracking based on Siamese network has made some progresses, that is it overcomes the limitation of the spatial invariance of Siamese network in the deep network. However, there are still factors such as appearance changes, scale changes, and occlusions that affect tracking performance. Focusing on the problems of large changes in object scale, object motion blur and small scale of object in the specified object tracking of Unmanned Aerial Vehicles (UAV), a new tracking algorithm was proposed based on the Siamese region proposal attention mechanism network, namely Attention-SiamRPN+. Firstly, an improved deep residual network ResNet-50 was employed as a feature extractor to extract feature maps. Secondly, the channel attention mechanism module was used to filter the semantic information of different channel feature maps extracted by the residual network, and the corresponding weights to different channel features were reassigned. Thirdly, a hierarchical fusion of two Region Proposal Networks (RPN) was applied. The RPN module was consisted of channel-by-channel deep cross-correlation of feature maps, classification of positive and negative samples and bounding box regression. Finally, the box of the object position was selected. In the test on the VOT2018 platform, the proposed algorithm had the accuracy of 59.4% and the Expected Average Overlap (EAO) of 39.5%. In the experiment with one-pass evaluation mode on the OTB2015 platform, the algorithm had the success rate and precision of 68.7% and 89.4% respectively. Experimental results show that the evaluation results of the proposed algorithm are better than the results of three excellent correlation filtering tracking and Siamese network tracking algorithms in recent years, and the proposed algorithm has good robustness and real-time processing speed when applying to the tracking of specified objects of UAV.

Key words: Siamese network, deep residual network, attention mechanism, Unmanned Aerial Vehicle (UAV), object tracking