基于尼斯特罗姆变压器的交叉模式变压器用于可见红外人重新识别
Ranjit Kumar Mishra1, Arijit Mondal2, Jimson Mathew2
1Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihta, Patna, 801106, Bihar, India. ranjit_2121cs30@iitp.ac.in.
Scientific reports
|May 9, 2025
概括
这项研究介绍了NiCTRAM,这是一种用于可见红外人重新识别的新方法. 通过有效地融合不同成像模式的特征,NiCTRAM在具有挑战性的条件下提高了准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的人重新识别 (Re-ID) 在不同的照明和夜间扎.
- 可见红外线 (VIS-IR) Re-ID在低光条件下利用红外线,但面临跨模式差异.
- 可见和红外数据之间的有效特征融合仍然是一个重大挑战.
研究的目的:
- 开发一个强大的框架,用于VIS-IR人重新识别.
- 解决和克服可见和红外图像之间的交叉模式差异.
- 在各种环境条件下提高个人再识别系统的准确性和可靠性.
主要方法:
- 拟议的NiCTRAM:一个基于Nyström变压器的交叉模式变压器.
- 利用共享的CNN骨干从RGB和IR图像中进行分层的特征提取.
- 采用并行Nyströmformer编码器,以有效地捕获远程依赖.
- 引入了交叉注意力融合块,集成了二次共变率统计数据以实现特征对齐.
主要成果:
- 在基准VIS-IR人重识别数据集上,NiCTRAM实现了最先进的性能.
- 与现有方法相比,显示出显著的改进,在排名-1准确度上超过SOTA高达5.90%,在mAP中超过5.83%.
- 在处理VIS-IR Re-ID.中固有的跨模式挑战方面表现出强大的稳定性.
结论:
- 尼克特拉姆有效地弥合了VIS-IR人重新识别的模式差距.
- 拟议的框架为具有挑战性的监控场景提供了强大而准确的解决方案.
- 该方法为需要在不同传感器上可靠的人员跟踪的实际应用提供了重大进步.
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