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| Multi branch Siamese network target tracking based on double attention mechanism |
Xiao-yan LI( ),Peng WANG*(),Jia GUO,Xue LI,Meng-yu SUN |
| School of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710021, China |
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Abstract A multi branch Siamese network target tracking algorithm based on dual attention mechanism was proposed in order to solve the problem of inaccurate positioning in the SiamRPN++ single target tracking algorithm when the target was briefly occluded and the appearance drastically changed. SiamRPN++ with lightweight backbone network was adopted as the basic algorithm. The algorithm was combined with lightweight channel and spatial attention mechanism in order to improve the anti-interference ability when dealing with occlusion challenges during the tracking process. A template branch was added from the previous frame, and the appearance changes of the target were dynamically updated. The ability to distinguish between foreground and background was enhanced during the tracking process using triplet loss. Local expansion search was conducted based on the speed of the target’s movement in order to enable timely and accurate tracking of the target even after short-term occlusion. The experimental results showed that the improved algorithm improved the success rate and precision of the OTB100 dataset by 2.4% and 1.6%, respectively, compared to the original algorithm. The average center position error decreased by 28.97 pixels, and the average overlap rate increased by 14.5%.
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Received: 20 June 2022
Published: 17 July 2023
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| Fund: 国家自然基金资助项目（62171360）；陕西省科技厅重点研发计划资助项目（2022GY-110）；2023年陕西省高校工程研究中心资助项目；2022年度陕西高校青年创新团队资助项目；山东省智慧交通重点实验室（筹） |
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Corresponding Authors:
Peng WANG
E-mail: 76469715@qq.com;wang_peng@xatu.edu.cn
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基于双注意力机制的多分支孪生网络目标跟踪
为了解决SiamRPN++单目标跟踪算法在目标被短时遮挡及外观剧烈变化时定位不准确的问题，提出基于双注意力机制的多分支孪生网络目标跟踪算法. 采用具有轻量化主干网络的SiamRPN++为基础算法，结合轻量化的通道和空间注意力机制，提升跟踪过程中应对遮挡挑战时的抗干扰能力. 新增上一帧模板分支，动态更新目标外观变化，利用三元组损失增强跟踪过程中前景与背景的判别能力. 根据目标的移动速度进行局部扩大搜索，使目标被短时遮挡后仍可以及时、准确地跟踪到目标. 实验结果表明，改进后的算法在OTB100数据集的成功率和精确度较原算法分别提高了2.4%和1.6%，平均中心位置误差降低了28.97个像素，平均重叠率提高了14.5%.
关键词：
孪生网络,
注意力机制,
模板更新,
局部扩大
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