计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 163-169.doi: 10.11896/jsjkx.210300066
赵越, 余志斌, 李永春
ZHAO Yue, YU Zhi-bin, LI Yong-chun
摘要: 针对传统孪生网络目标跟踪算法在相似物干扰、目标形变、复杂背景等跟踪环境下无法进行鲁棒跟踪的问题,提出了注意力机制指导的孪生网络目标跟踪方法,以弥补传统孪生跟踪方法存在的性能缺陷。首先,利用卷积神经网络ResNet50的不同网络层来提取多分辨率的目标特征,并设计互注意力模块使模板分支与搜索分支之间的信息能够相互流动。然后,在分类与回归网络中,将主干网络提取的每块特征信息权重参数通过神经网络自动学习、更新并加权融合每块特征的分类与回归信息。最后,根据响应图的峰值位置计算目标的预估位置和尺度信息。在UAV123数据集上,所提算法相比主流跟踪算法SiamBAN,准确率提升了1.7个点,成功率提升了0.7个点;在VOT2018数据集上,相比SiamRPN++算法,所提算法在EAO指标上提高了2.5个点,实时跟踪速度保持在35FPS。
中图分类号:
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