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基于tr-PCBAMSiam的小目标跟踪算法

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  • NETL
  • NSTL
  • 万方数据
  • 维普
无人机在目标跟踪过程中,存在分辨率低、运动模糊、目标遮挡、目标密集、相似目标干扰等问题,导致算法跟踪精度下降.针对这一问题,在SiamRPN的基础上提出tr-PCBAMSiam,即一种基于混合注意力的聚合残差连接、transformer互相关运算以及基于无锚框的区域回归网络的目标跟踪算法.将该算法与其他目标跟踪算法在OTB100数据集上进行对比,在精度和成功率方面,与SiamRPN算法相比分别有 6.9%和 8%的提升;在LaTOT数据集上与SiamRPN相比,精度和成功率分别有 13.1%和 8.5%的提升.
Small target tracking algorithm based on tr-PCBAMSiam
During the target tracking process of drones,there are problems such as low resolution,motion blur,target occlusion,dense target,interference of similar targets,etc.,resulting in a decrease in algorithm tracking accuracy.To solve this problem,tr-PCBAMSiam was proposed on the basis of SiamRPN,which was a target tracking algorithm of aggregated residual connection based on mixed attention,transformer cross-correlation operation and region regression network without anchor frame.The algorithm of this study was compared with other target tracking algorithms on the OTB100 dataset.In terms of accuracy and success rate,there were 6.9%and 8%improvements respectively compared with the SiamRPN algorithm;compared with SiamRPN on the LaTOT dataset,the accuracy and the success rates were increased by 13.1%and 8.5%respectively.

target trackingaggregate residual connectiontransformer cross-correlationtwin networkfeature fusion

苏冲、雷斌、蒋林、汪杰、李港

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武汉科技大学 机械自动化学院,湖北 武汉 430081

武汉科技大学 机器人与智能系统研究院,湖北 武汉 430081

目标跟踪 聚合残差连接 transformer互相关 孪生网络 特征融合

国家重点研发计划湖北省自然科学基金武汉市应用基础前沿项目机器人与智能系统研究院开放基金

2019YFB13100002018CFB6262019010701011404F201803

2024

10.3969/j.issn.1673-3142.2024.01.028

农业装备与车辆工程
山东省农业机械科学研究所 山东农机学会

农业装备与车辆工程

影响因子：0.279
ISSN：1673-3142
年,卷(期)：2024.62(1)
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