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锡安FDA:功能动态激活锡安网络用于视觉跟踪

Jialiang Gu1, Ying She2, Yi Yang2

  • 1Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510000, China. gujliang@mail2.sysu.edu.cn.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 当前基于罗网络的视觉跟踪算法往往忽略了全球空间信息.
  • 这种限制导致稳定性降低,特别是在具有不可靠对象区域的场景中.

研究的目的:

  • 引入一个新的无视觉跟踪框架,特征动态激活语网络 (SiamFDA).
  • 通过整合全球空间信息和提高可靠性来解决现有方法的局限性.

主要方法:

  • 开发了SiamFDA,这是一个框架,可以捕捉远距离像素之间的远程依赖关系.
  • 实现了分层特征选择器,用于在不同层面上进行自适应性特征激活.
  • 引入了适应性样本标签分配方法,以优化培训.

主要成果:

  • 与最先进的追踪器相比,SiamFDA在六个基准数据集 (VOT-2018,VOT-2019,GOT10k,LaSOT,OTB-2015,OTB-2013) 中表现优越.
  • 该框架实现了每秒40的实时处理速度.
  • 在具有挑战性的跟踪场景中实现了更好的稳定性.

结论:

  • SiamFDA在无视觉跟踪方面取得了重大进展.
  • 提出的方法有效地利用全球空间信息和适应性特征选择,以提高性能.
  • SiamFDA为实时视觉对象跟踪提供了强大而高效的解决方案.