基于视频的个人重新识别与互补的本地和全球特征使用图形变压器
Hai Lu1, Enbo Luo1, Yong Feng1
1Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650217, China.
Mathematical biosciences and engineering : MBE
|August 23, 2024
概括
本研究引入了一个图形变压器模型,通过捕捉地方区域之间的关系来改善视频人重新识别 (Re-ID). 这种新的方法增强了特征表示,以便在视频中更准确地匹配人.
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:
- 基于视频的个人重新识别 (Re-ID) 对监视和安全至关重要.
- 当前的Re-ID方法难以提供强大的特征表示,特别是忽视了本地区域的相关性.
- 提取歧视性和强大的人格特征仍然是视频Re-ID的关键挑战.
研究的目的:
- 为视频人Re-ID.提出一种新的表示学习方法.
- 为了有效地建模和利用视频中的地方区域之间的相关性.
- 增强人格特征的辨别力和稳定性,以提高Re-ID准确性.
主要方法:
- 使用图形变压器来建模本地区域之间的关系,促进上下文特征交互.
- 构建局部关系图表以表示局部区域 (节点) 之间的内在关系.
- 一个基于视觉转换器的全球特征学习分支捕捉了框架间的关系,通过双分支交互网络集成,以多头融合为重点.
主要成果:
- 拟议的图形变压器方法有效地模拟了当地地区之间的关系.
- 通过双分支网络整合本地和全球特征,增强了代表性学习.
- 在iLIDS-VID,MARS和DukeMTMC-VideoReID数据集上的实验结果显示出具有竞争力的性能.
结论:
- 这种基于图形变压器的新方法通过捕捉区域间的相关性,显著改善了视频人重新识别.
- 双分支网络有效地融合了本地和全球特征,实现了强大的个人代表性.
- 该方法验证了利用关系信息提高视频人Re-ID性能的有效性.
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