National Key Research and Development Project of China (2019YFB2102500)
在现实的智慧城市安全场景中，传统的行人重识别方法已经难以满足复杂多样的识别任务要求。为实现多层次的行人重识别，该文提出将行人重识别技术与多层次的城市信息单元深度融合。在行人重识别任务中，现有的模型和注意力只关注鲁棒特征的学习，而该文基于特征向量差异，提出了差异注意力模块，以增强深度特征的判别力。结合差异注意力模块，该文开发了与多种骨干模型适配的差异注意力框架。此外，该文还提出了联合训练和单独训练两种训练策略。与其他行人重识别方法相比，差异注意力框架和训练策略在 Market-1501、CUHK03 和 MSMT17 数据集上均取得了更优的性能。
The traditional person re-identification methods are difficult to independently cope with the complex and diverse recognition tasks in the security scenario of smart city in practice. In order to meet the needs of multi-level person re-identification, the deep integration of person re-identification and multi-level urban information units is proposed. Existing models and attentions for person re-identification tasks only focus on learning the robust features while neglecting the difference between features of pairs. Diff attention module is proposed to guide the network to learn a more discriminative attention map based on the difference of feature vectors. Taking the diff attention module, diff attention framework which matches many backbone models is developed. Two training strategies: joint training and separate training are proposed. Compared with other person re-identification methods, these framework and strategies have achieved excellent performance on Market-1501, CUHK03, and MSMT17 datasets.
ZHU Li, LIN Xin, XU Yifei, et al. Multi-level Person Re-identification based on Urban Information Unit and Diff Attention Scheme[J]. Journal of Integration Technology,2023,12(1):91-104