摘要
提出一种基于注意力机制的特征融合孪生网络目标跟踪算法。针对目标跟踪算法特征提取网络深度较浅导致特征鲁棒性不足的问题,使用改进后的ResNet-50网络提取模板帧和搜索帧图像的深层和浅层特征,并利用通道和空间注意力机制对提取得到的深浅层特征进行融合。针对目标跟踪算法仅使用首帧图像作为模板导致模板失效、跟踪漂移等问题,在传统孪生网络中增加一条模板分支以将首帧和搜索帧前一帧图像共同作为目标模板。与传统经典的跟踪方法相比,提出的算法在OTB100和VOT2016数据集的相关实验获得了最佳的性能表现,验证了提出算法的有效性和可行性。
An object tracking algorithm for feature fusion Siamese network based on attention mechanism is proposed.Aiming at the problem of insufficient feature robustness caused by the shallow depth of the feature extraction network of the object tracking algorithm,using the improved ResNet-50 network to extract the deep and shallow layers features of the template frame and search frame images,and using the channel and spatial attention mechanisms to fuse the extracted deep and shallow features.A template branch is added to the traditional Siamese network,and the first frame and the previous frame of the search frame are used as the object template to deal with the problem of template failure and tracking drift caused by the object tracking algorithm only using the first frame image as a template.Compared with the traditional classical tracking methods,the proposed algorithm has obtained the best tracking performance in the related experiments on the OTB100 and VOT2016 dataset,which verifies the effectiveness and feasibility of the proposed algorithm.
作者
石健彤
王瑜
毕玉
肖洪兵
孙梅
SHI Jiantong;WANG Yu;BI Yu;XIAO Hongbing;SUN Mei(School of Artificial Intelligence,Beijing Technology and Business University,Beijing 100048,China)
出处
《传感器与微系统》
CSCD
北大核心
2024年第8期145-149,共5页
Transducer and Microsystem Technologies
基金
北京市自然科学基金-北京市教育委员会科技计划重点项目(KZ202110011015)。
关键词
目标跟踪
孪生网络
特征提取
特征融合
注意力机制
object tracking
Siamese network
feature extraction
feature fusion
attention mechanism
