基于双分支在线优化和特征融合的视频目标跟踪算法OA北大核心CSTPCD

In response to the issue of inadequate discrimination capability in the D3S algorithm for tracking target,a video object tracking algorithm based on dual-branch online optimization and feature fusion is proposed.Firstly,a dual-branch online optimization classifier is constructed,which achieves secondary location of the target,resulting in a more accurate target position response map.Secondly,the fusion of the response map and search features is realized on the feature layer,and the encoder module promotes the fusion process,further highlighting the features of tracking target.Finally,by updating the template features with the encoder module,the differences between features are fitted,thereby enhancing the discriminative capability of the segmentation module.Experimental evaluations are conducted on the VOT2018 and UAV123 datasets.Compared with the original algorithm,the improved algorithm improves EAO by 2.9%on the VOT2018 dataset,increases success rate by 2.4%and accuracy by 2.9%on the UAV123 dataset.The experimental results demonstrate that the method in this paper improves the algorithm's discriminative ability and further improves accuracy and robustness.