联合模式对齐和特征增强可见红外人重新识别
Ronghui Lin1, Rong Wang1,2, Wenjing Zhang1
1School of Information and Cyber Security, People's Public Security University of China, Beijing 100038, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种用于可见红外人重新识别的新方法,增强了交叉模式对齐和特征提取. 这种方法显著提高了不同相机视图和模式的个人匹配精度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 模式识别 模式识别
背景情况:
- 可见红外人重新识别解决了在各种相机系统和感觉模式 (可见光与红外) 中匹配个体的挑战.
- 当前的重新识别技术往往优先考虑跨模式对齐,可能会忽视为最佳性能提供关键功能增强策略.
- 有效的人重新识别对于监控,安全和智能运输系统至关重要.
研究的目的:
- 通过整合模式对齐和功能增强来开发可见红外人重新识别的改进方法.
- 解决现有方法的局限性,这些方法忽视了跨模式人匹配中的功能增强.
- 在可见红外人重新识别任务中实现最先进的性能.
主要方法:
- 可见红外模态数据增强 (VIMDA) 是为了提高可见和红外图像之间的对齐而引入的.
- 边际MMD-ID损失被用来进一步完善模式调整,并在培训期间加快模式融合.
- 建议采用多粒度特征提取 (MGFE) 结构,以显著提高特征表示和识别精度.
主要成果:
- 与SYSY-MM01和RegDB数据集的现有最先进方法相比,提出的方法显示出更高的性能.
- 实验证实了个体成分的有效性,包括VIMDA和MGFE,通过综合性废除研究.
- 综合方法成功地改善了人重新识别的跨模式对齐和特征可歧视性.
结论:
- 增强模式对齐和特征提取的联合战略在可见红外人重新识别方面取得了重大进展.
- 拟议的VIMDA和MGFE技术有效地提高了人与人匹配的稳定性和准确性.
- 这项研究为开发更复杂,更准确的人重新识别系统提供了有希望的方向.
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