一个基于自蒸的多级统一追踪器,用于高效的RGB-T追踪
概括
本研究介绍了用于RGB-热 (RGB-T) 追踪的多级自蒸 (MSD) 框架. 通过有效地融合RGB和热数据而无需复杂的网络更改,MSD提高了跟踪精度,提高了效率.
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:
- RGB-热 (RGB-T) 追踪结合了RGB和热红外 (TIR) 数据,以提高视觉追踪的稳定性.
- 现有的RGB-T追踪器经常使用复杂的架构,阻碍了效率.
研究的目的:
- 为高效的RGB-T跟踪提出一个新的多级自蒸 (MSD) 框架.
- 为了适应一个单流RGB跟踪器的RGB-T设置,没有修改架构或额外的参数.
主要方法:
- 通过共享的骨干系统共同处理RGB和TIR输入.
- 使用自我监督 (对比损失,模式差距对齐损失) 和监督目标 (中间焦点损失,模式特定损失,融合跟踪损失) 的组合.
主要成果:
- 在LasHeR,RGBT234和GTOT基准上实现了最先进的跟踪精度.
- 保持了原来的RGB跟踪器的计算效率.
结论:
- 优化的培训策略可以在多式联运跟踪中超越复杂的架构修改.
- MSD框架为现实世界的部署提供了显著的实际优势.
相关概念视频
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