通过适应性道选择来学习时间规范化的空间意识深度相关性过器跟踪
Sathiyamoorthi Arthanari1, Dinesh Elayaperumal1, 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新型深度相关过器,增强了对阻塞和杂乱的稳定性. 该方法利用自适应性通道选择和时间规范化,在具有挑战性的场景中提高准确性和适应性.
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:
- 深度关联过器在对象跟踪方面表现出色,但在阻塞,目标偏差和背景杂乱方面扎.
- 现有的方法往往无法有效利用历史目标信息,从而限制了跟踪性能.
研究的目的:
- 提出一种新的时间规范化的空间意识深相关性过器跟踪方法.
- 为了提高对象跟踪的稳定性,准确性和适应性,在具有挑战性的场景,如阻塞和杂乱.
主要方法:
- 适应性道选择,用于处理目标偏差和动态波器调整.
- 具有空间意识的相关过器,具有动态空间约束来区分前景和背景.
- 使用当前和以前的来提高与外观变化的准确性.
主要成果:
- 在包括OTB-2013,OTB-2015和UAVDT在内的多个基准数据集中证明了有效性.
- 在具有挑战性的追踪场景中,超越了最先进的追踪器.
- 通过适应性道选择和时间规范化,实现了更好的准确性和稳定性.
结论:
- 拟议的方法显著提高了对象跟踪性能.
- 适应性道选择和时间规范化是克服跟踪挑战的关键.
- 该方法为各种现实世界跟踪应用提供了灵活而强大的解决方案.
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