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规模感知跟踪方法与外观特征过和框架间连续性.

Haiyu He1, Zhen Chen1, Zhen Li1

  • 1School of Automation, Beijing Institute of Technology, Beijing 100010, China.

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|September 9, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种高效的视觉对象跟踪方法,可以处理尺度变化而不需要多尺度特征. 该方法提高了区分相关性波器 (DCF) 追踪器的性能,并降低了实时应用程序的计算成本.

关键词:
颜色名称 颜色名称 颜色名称区分相关性过器的区分相关性过器.突出的特征 突出的特征规模估计规模的估计.视觉跟踪 视觉跟踪 视觉跟踪

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 视觉对象跟踪在计算机视觉中至关重要,但尺度变化构成了重大挑战.
  • 基于歧视性关联波器 (DCF) 的追踪器与规模变化作斗争,特别是在计算约束下.
  • 现有的规模估计的多尺度特征方法是计算密集的,阻碍了实时性能.

研究的目的:

  • 开发用于视觉对象跟踪的实用和高效的解决方案,以解决尺度变化.
  • 创建一个与任何基于DCF的追踪器兼容的插件模块,增强强性而不需要多尺度功能.
  • 为了降低DCF追踪器的计算成本,同时保持高精度和实时性能.

主要方法:

  • 利用颜色名称 (CN) 特性和突出特征来减少目标外观模型的维度.
  • 估计目标规模使用高斯分布模型,结合全球和本地规模一致性假设.
  • 合并的拟议规模估计与DCF跟踪结果,以更新目标位置和规模.

主要成果:

  • 在Temple Color 128基准数据集上实现了竞争力的准确性和稳定性.
  • 与现有方法相比,显著降低了计算成本.
  • 证明了拟议方法作为DCF追踪器插件模块的有效性.

结论:

  • 拟议的方法提供了一个高效和实用的解决方案,用于视觉对象跟踪与尺度变化.
  • 这种方法可以提高DCF跟踪器的性能,因为它可以有效地处理规模变化,而无需使用多个规模的功能.
  • 这种方法适用于具有有限计算资源的实时应用.