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    虚实结合的行人重识别方法

    Person Re-identification Method Based on Hybrid Real-Synthetic Data

    • 摘要: 近年来,随着城市化进程的加速和社会经济的发展,公共安全问题也愈发引起人们的关注. 为了保障社会稳定和公民生命财产安全,各地政府开始大力推进智能安防和智慧城市的建设. 行人重识别就是构建智慧城市的核心技术之一,对安防监控和刑事调查申请具有重要意义. 行人重识别旨在检索不同摄像头下捕捉到的特定人物. 然而,由于光照、视角、遮挡和姿势等造成的类内差异,行人重识别在计算机视觉领域仍然是一项具有挑战性的任务. 受限于数据和标记匮乏,已有的全监督行人重识别任务在模型层面上改进的方法效果基本达到瓶颈. 引入更复杂多样且标记易获得的大型虚拟数据集来进行辅助训练,并提出了一种基于摄像头感知的非对称领域对抗学习方法,同时缓解领域间差异和多摄像头间类内差异的影响,使模型从更丰富多样的数据中学到摄像头差异无关的特征表示. 此外,为了缓解虚拟数据集夹带的误导信息带来的不利影响和对抗训练中真实世界数据集的分布向虚拟数据集的数据分布发生偏移的问题,提出使用基于真实数据训练的辅助网络来约束主干网络的训练. 实验在2个公开的数据集上进行验证,表明了该方法的有效性.

       

      Abstract: In recent years, the rapid urbanization and development of the social economy have led to a growing focus on public safety issues. Governments across the world are increasingly promoting the construction of smart cities and intelligent security systems to safeguard the lives and property of citizens and maintain social stability. Person re-identification (ReID) is an essential technology for building smart cities, with significant implications for security monitoring and criminal investigation applications. The goal of person re-identification is to accurately identify specific individuals captured under different cameras. However, due to intra-class differences resulting from various factors such as illumination, viewpoint, occlusion, and pose, person re-identification remains a challenging task in the field of computer vision. Although existing fully supervised person re-identification methods have made significant progress, the scarcity of data and labels poses a bottleneck to further improving model performance. To address this challenge, this paper introduces a more complex and diverse synthetic dataset with easy-to-obtain labels for auxiliary training, and proposes a novel Camera-aware Asymmetric Adversarial Learning (CAAL) method that overcomes intra-class variation among multiple cameras and the domain-shift between real and synthetic data, enabling the learning of camera-invariant feature representations from diverse data sources. Furthermore, to mitigate the impact of misleading information carried by synthetic datasets and prevent the model from overfitting to synthetic data during adversarial training, we propose using an auxiliary network trained on real-world data to constrain the training of the backbone network. We conduct extensive experiments on two public datasets to demonstrate the effectiveness of the proposed method.

       

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