通过自适应式混合模型学习多规范化的突变感知相关性过器,用于通过自适应式混合模型对象跟踪
Sathiyamoorthi Arthanari1, Jae Hoon Jeong1, Young Hoon Joo1
1School of IT Information and Control Engineering, Kunsan National University, 558 Daehak-ro, Gunsan-si, Jeonbuk 54150, Republic of Korea.
概括
这项研究引入了一种用于对象跟踪的多规范化突变感知相关性过器 (MRMACF). 该MRMACF方法有效地处理外观突变和目标扭曲,在具有挑战性的场景中提高追踪精度和可靠性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 区分相关过器 (DCF) 对于对象跟踪是有效的,但与外观突变,过器退化和目标扭曲作斗争.
- 由于这些挑战,现有的DCF追踪器面临性能下降,需要提高稳定性.
研究的目的:
- 引入一种新的多规范化突变感知相关性过器 (MRMACF),以解决基于DCF的对象跟踪的局限性.
- 为了增强追踪器对外观变化,过器退化和目标扭曲的弹性.
主要方法:
- 开发了一种具有适应性混合模型和突变威胁机制的突变意识策略,以处理外观突变和过器退化.
- 实施了改进的稀疏空间特征选择,结合行/列方法来解决目标扭曲.
- 引入了周围意识的方法,以利用上下文信息并防止过器偏差.
主要成果:
- 与基准数据集上的现代追踪器相比,MRMACF方法显示出更高的性能.
- 在OTB-2015数据集中实现了最高的性能,DP得分为93.2%和AUC得分为69.8%.
结论:
- 拟议的MRMACF方法通过有效地减轻外观突变,过器退化和目标扭曲,显著提高了对象跟踪性能.
- 对于具有挑战性的对象跟踪任务,MRMACF提供了强大而高效的解决方案.
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