基于多支路的孪生网络目标跟踪

Siamese Network Target Tracking Based on Multiple Branches

为有效解决目标跟踪在面对大尺度形变、完全遮挡、背景干扰等复杂场景时出现漂移或者跟踪丢失的问题,本文提出了一种基于多支路的孪生网络目标跟踪算法(SiamMB).首先,通过增加邻近帧支路的网络鲁棒性增强方法以提高对搜索帧中目标特征的判别能力,增强模型的鲁棒性.其次,融合空间注意力网络,对不同空间位置的特征施加不同的权重,并着重关注空间位置上对目标跟踪有利的特征,提升模型的辨别力.最后,在OTB2015和VOT2018数据集上的进行评估,SiamMB跟踪精度和成功率分别达到了91.8%和71.8%,相比当前主流的跟踪算法取得了良好的竞争力.

In order to effectively solve the problem of target tracking drift or loss in the face of large-scale deformation,complete occlusion,background interference,and other complex scenes,a multi-branch Siamese network target tracking algorithm(SiamMB)is proposed.First,the method of enhancing the network robustness of adjacent frame branches is used to improve the discrimination ability of target features in the search frame and strengthen the robustness of the model.Secondly,the spatial attention network is fused,and different weights are applied to the features of different spatial positions.In addition,the features that are beneficial to target tracking in spatial positions are emphasized,so as to improve the discriminability of the model.Finally,evaluation is performed on OTB2015 and VOT2018 datasets,and the results show that the tracking accuracy and success rate of SiamMB reach 91.8%and 71.8%,respectively,which makes SiamMB more competitive than the current mainstream tracking algorithms.