Wu Duidi, Huang Haiqing, Zhao Qianyou, Zhang Shuo, Qi Jin, Hu Jie
Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
Heliyon. 2022 Nov 30;8(12):e12086. doi: 10.1016/j.heliyon.2022.e12086. eCollection 2022 Dec.
Pedestrian attribute recognition (PAR) and re-identification (ReID) are important works in the area of computer vision, which are widely used in intelligent surveillance and are of great significance to the creation of smart life. The purpose of this article is to focus on organizing a review of ReID based on deep learning and analyze the associations between PAR and ReID. Firstly, we summarize the major ideas of Attribute-Assisted ReID and compare the differences in datasets and algorithmic concerns between the two areas. Secondly, we introduce a wide range of representative ReID methods. By analyzing some cutting-edge researches, we summarize their specific network structure, loss function design, and effective training tricks. Reference methods and solutions are provided for the main challenges of ReID, such as cloth-changing, domain adaptation, occlusion condition, resolution changes, etc. Finally, we conclude the performance and characteristics of the SOTA methods, obtain inspiration and prospects for future research directions, and demonstrate the effectiveness of Attribute-Assisted ReID.
行人属性识别（PAR）和再识别（ReID）是计算机视觉领域的重要工作，广泛应用于智能监控，对创造智能生活具有重要意义。本文旨在重点对基于深度学习的ReID进行综述，并分析PAR和ReID之间的关联。首先，我们总结了属性辅助ReID的主要思想，并比较了这两个领域在数据集和算法关注点上的差异。其次，我们介绍了广泛的代表性ReID方法。通过分析一些前沿研究，我们总结了它们具体的网络结构、损失函数设计和有效的训练技巧。针对ReID的主要挑战，如换衣、域适应、遮挡情况、分辨率变化等，提供了参考方法和解决方案。最后，我们总结了当前最优方法的性能和特点，获得了对未来研究方向的启发和展望，并证明了属性辅助ReID的有效性。
