基于多重注意力机制与响应融合的孪生单目标跟踪算法  

Siamese single-object tracking algorithm based on multiple attention mechanisms and response fusion

作　　者：冯文亮 孟凡宝[1] 余川[1] 游安清[1] Feng Wenliang;Meng Fanbao;Yu Chuan;You Anqing(Institute of Applied Electronics,CAEP,Mianyang 621900,China;Graduate School of China Academy of Engineering Physics,Mianyang 621900,China)

机构地区：[1]中国工程物理研究院应用电子学研究所,四川绵阳621990 [2]中国工程物理研究院研究生院,四川绵阳621900

出　　处：《强激光与粒子束》2024年第8期140-148,共9页High Power Laser and Particle Beams

基　　金：中国工程物理研究院高功率微波实验室基金项目。

摘　　要：针对孪生全卷积网络的单目标跟踪算法因无法提取到目标的高层语义特征和无法一次性集中关注并学习到目标的通道、空间及坐标特征导致在复杂场景下面临目标形变、姿态变化及背景干扰等挑战时,出现跟踪性能下降以及跟踪失败的问题,提出了一种基于多重注意力机制与响应融合的孪生网络单目标跟踪算法用来解决这一问题。在该算法中设计了小卷积核与跳层连接特征融合的深层骨干特征提取网络、改进型注意力机制及卷积互相关后的响应融合运算这3个模块用来提升该算法的跟踪性能,并通过消融实验验证了这3个模块的有效性。最后,经在OTB100基准数据集上测试,跟踪精确度达到了0.825,跟踪成功率达到了0.618。同时与其他先进算法进行对比,结果表明该算法不仅可以有效应对复杂场景下目标跟踪算法性能下降的问题,还可以在保证跟踪速度的前提下,进一步提高跟踪的精度。In this paper,to address the problem that the single-object tracking algorithm of Siamese fully convolutional networks cannot extract the high-level semantic features of the object and cannot focus on and learn the channel,spatial and coordinate features of the object at one time,which leads to degradation of the tracking performance and tracking failures when faced with the challenges of the object's deformation,attitude changes,and background interference in a complex scenario,we propose a single-object tracking algorithm for Siamese networks based on the multiple-attention mechanism and response fusion.In this algorithm,three modules,namely,the backbone feature extraction network with small convolutional kernel fused with jump-layer connected features,the improved attention mechanism,and the response fusion operation after convolutional inter-correlation are designed to enhance the tracking performance of this algorithm,and the effectiveness of these three modules is verified by ablation experiments.Finally,after testing on the OTB100 benchmark dataset,the tracking accuracy reaches 0.825,and the tracking success rate reaches 0.618.Meanwhile,compared with other advanced algorithms,it shows that the algorithm not only can effectively cope with the problem of decreasing performance of object tracking algorithms in complex scenarios,but also can further improve the tracking accuracy under the premise of guaranteeing the tracking speed.

关 键 词：孪生网络 单目标跟踪 注意力机制 特征响应 融合 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]