针对可见红外人重新识别的跨模组组关系优化
Jianqing Zhu1, Hanxiao Wu2, Yutao Chen1
1College of Engineering, Huaqiao University, Quanzhou, China.
概括
本研究引入了一种用于可见红外人重新识别 (VIPR) 的新方法,该方法优化了跨模式组关系,以减少外观差异. 该方法通过将可见图像和红外图像之间的差异最小化,显著提高了识别准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 模式识别 模式识别
背景情况:
- 可见红外人重新识别 (VIPR) 对智能交通系统至关重要.
- 可见和红外图像之间的模态差异阻碍了准确的外观歧视.
- 现有的方法通常需要复杂的网络来解开模态和外观差异.
研究的目的:
- 提出一种新的方法来优化VIPR的模式差异,而不需要复杂的解.
- 引入一种跨模式集团关系 (CMGR) 度量来衡量模式差异.
- 开发一个群关系相关性 (GRC) 损失函数以优化CMGR.
主要方法:
- 开发了一种跨模组组关系 (CMGR) 度量,以捕捉可见和红外模式中个人之间的关系.
- 设计了一个组关系相关性 (GRC) 损失函数,利用皮尔森相关性,以优化CMGR.
- 集成的CMGR作为培训阶段的约束,以尽量减少模式差异.
主要成果:
- 与对RegDB和SYSU-MM01数据集的最先进方法相比,CMGR方法显示出更高的性能.
- 在使用CMGR时,在RegDB数据集上的排名-1识别率取得了超过7%的显著改善.
- CMGR模型有效地将模式差异最小化,而无需在推理过程中执行.
结论:
- 拟议的CMGR方法为解决VIPR模式差异提供了有效和高效的解决方案.
- 优化跨模式的群体关系为提高人重新识别准确性提供了一个有希望的方向.
- CMGR作为一个有价值的培训约束,显著提高了VIPR系统的性能.
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