计算机科学 ›› 2021, Vol. 48 ›› Issue (4): 123-129.doi: 10.11896/jsjkx.200800164
程旭1,2, 崔一平1,2, 宋晨1,2, 陈北京1,2, 郑钰辉1,2, 史金钢3
CHENG Xu1,2, CUI Yi-ping1,2, SONG Chen1,2, CHEN Bei-jing1,2, ZHENG Yu-hui1,2, SHI Jin-gang3
摘要： 目标跟踪技术在智能监控、人机交互、无人驾驶等诸多领域得到了广泛的应用。近年来,学者们提出了许多高效的算法。然而,随着跟踪环境越来越复杂,目标跟踪算法在遮挡、光照变化、背景干扰等复杂环境下仍然面临着巨大的挑战,从而导致目标跟踪失败。针对上述问题,提出了一种基于时空注意力机制的目标跟踪算法。首先,采用孪生网络架构来提高对特征的判别能力;然后,引入改进的通道注意力机制和空间注意力机制,对不同通道和空间位置的特征施加不同的权重,并着重关注空间位置和通道位置上对目标跟踪有利的特征。此外,还提出了一种高效的目标模板在线更新机制,将第一帧图像特征与后续跟踪图像帧中置信度较高的图像特征进行融合,以降低发生目标漂移的风险。最后,在OTB2013和OTB2015数据集上对所提跟踪算法进行了测试。实验结果表明,所提算法的性能相比当前主流的跟踪算法提高了6.3%。
中图分类号:
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