基于核心化相关性波器的实时追踪目标系统,用于复杂区域
Abdel Hamid Mbouombouo Mboungam1, Yongfeng Zhi1, Cedric Karel Fonzeu Monguen2
1School of Automation, Northwestern Polytechnical University, Xi'an 710002, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
这项研究通过引入尺度金字塔和改进漂移抑制以提高复杂环境中的精度和弹性来增强使用核心化相关性过器 (KCF) 的实时对象跟踪.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 实时对象跟踪对于图像引导技术至关重要,但在复杂的环境中面临挑战.
- 现有的核相关性跟踪算法平衡速度和性能,但与模型漂移和规模变化作斗争.
研究的目的:
- 开发一种改进的对象跟踪方法,解决核心化相关性过器 (KCF) 的局限性.
- 在具有挑战性的场景中增强实时跟踪精度和稳定性.
主要方法:
- 研究了核心化相关性过器 (KCF),包括模型训练和密集采样.
- 开发了一个规模金字塔方法来处理目标规模转换.
- 对KCF的改进包括用于抑制漂移的初始对象模型和用于故障恢复的对象检测模块.
主要成果:
- 规模金字塔方法有效地解决了目标尺寸变化问题.
- 改进的KCF表现出减少模型漂移和增强的长期跟踪能力.
- 一个评估系统证实了与标准KCF和其他跟踪方法相比,拟议的方法的卓越准确性和弹性.
结论:
- 提议的增强的核心化相关性过方法显著改善了实时对象跟踪性能.
- 综合规模金字塔和漂移抑制技术为复杂的跟踪任务提供了强大的解决方案.
- 这项工作为需要可靠的物体跟踪的图像引导技术提供了宝贵的进步.
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