Abstract: In order to solve the problem of tracking drift due to target occlusion and tracking failure due to background interfe-rence during target tracking,this paper proposes a siamese network-based object tracking method with multi-feature integration,where feature fusion and attention mechanism are introduced to build multiple region-proposal-network based tracking modules.Firstly,two adjacent residual block are squeeze-and-excitation and then effectively fused,as a way to strengthen the feature information.Secondly,the parallel convolution attention module is used to filter the interference information contained in the channel information and spatial information.Finally,an algorithm similar to ensemble learning is proposed by constructing two different trackers,which receive deep semantic features and the aforementioned fused features,respectively,and weight them and train for the final object tracking.In addition,to verify the effectiveness of the algorithm,this paper also investigates the effects of diverse fusion schemes,different training weights to each tracker and the combination ways of the modules in the proposed model.Experi-mental results on the VOT2016 and VOT2018 datasets show that the proposed multi-feature integration method can effectively improve the robustness of the object tracking compared with other siamese network-based object tracking algorithms,while ensuring high accuracy.

Key words: Object tracking, Convolutional neural networks, Siamese networks, Feature fusion, Attention mechanisms