基于tr-PCBAMSiam的小目标跟踪算法

Small target tracking algorithm based on tr-PCBAMSiam

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。

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.