跨模式人重新识别方法与联合模式生成和功能增强.
Yihan Bi1, Rong Wang1,2, Qianli Zhou3
1School of Information and Cyber Security, People's Public Security University of China, Beijing 100038, China.
Entropy (Basel, Switzerland)
|August 29, 2024
概括
本研究引入了一种使用模式生成和特征增强的新型跨模式人重新识别方法. 这种方法显著提高了可见和红外行人识别任务的准确性.
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:
- 可见和红外 (IR) 行人重新识别 (re-ID) 面临着由于模式差异的挑战.
- 在不同的传感器中有效的特征表示对于准确的重新识别至关重要.
研究的目的:
- 提出一种跨模式的人重新识别方法,尽量减少可见-红外线差异.
- 增强行人特征表示,以提高重新识别性能.
主要方法:
- 一个轻量级的网络可见的图像尺寸缩小和增大,以产生中间模式.
- 将卷积块注意模块 (CBAM) 集成到ResNet50中,以增强功能强调.
- 将梯度集中纳入随机梯度下降 (SGD) 优化器,以实现更快的融合和更好的泛化.
主要成果:
- 在SYSU-MM01和RegDB数据集上显著提高了性能.
- 排名-1的准确性分别增加了7.12%和6.34%.
- 平均精度 (mAP) 分别提高了4.00%和6.05%.
结论:
- 拟议的方法有效地弥合了可见和红外模式之间的差距.
- 集成CBAM和渐变集中化可以提高跨模式重新识别的网络性能.
- 该方法在跨越多种数据集的行人重新识别方面显示出卓越的结果.
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