通过注意力驱动的全球特征和角度损失优化来增强个人重新识别
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)
|June 26, 2024
概括
本研究引入了使用先进的深度学习技术改进的人重新识别方法. 这种新的方法增强了行人特征提取,从而在复杂的场景中实现更准确的识别.
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:
- 步行者重新识别 (re-ID) 在准确表示和区分步行者属性方面面临挑战.
- 现有的方法在特征表示和分类准确性方面扎.
研究的目的:
- 提出一种用于重新识别人的新方法,以提高特征表示和分类准确度.
- 提高行人特征的可辨别性,以实现更强大的识别.
主要方法:
- 基于规范化的通道注意模块与ResNet50骨干的集成,以优先考虑关键的行人特征.
- 使用动态激活函数以适应调节ReLU参数,增强非线性表达.
- 在监督培训中将弧面损失与交叉损失结合起来,促进类间的差异和类内的一致性.
主要成果:
- 在市场1501数据集上,排名-1准确度提高了1.28%.
- 在DukeMTMC-ReID数据集上获得了1.4%的排名-1准确度增加.
- 在各自的数据集上,平均平均精度 (mAP) 得到了1.93%和1.84%的改善.
结论:
- 拟议的模型有效地提取了强大的行人特征,增强了特征的可区分性.
- 该方法在人员重新识别任务中带来了更高的识别准确性.
- 该方法解决了行人属性表示和歧视的局限性.
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