论文标题
无监督的领域自适应人员重新识别具有多相机约束
Unsupervised domain-adaptive person re-identification with multi-camera constraints
论文作者
论文摘要
人重新识别是分析基于视频的人类行为的关键技术；但是，由于与培训数据中不同的域的性能下降，因此在实际情况下的应用仍然具有挑战性。在这里，我们提出了一个受环境受限的自适应网络，以减少域间隙。该网络通过施加多相机约束来完善通过自我训练方案估算的伪标签。所提出的方法将从环境获得的没有人身份标签的人对信息包含到模型培训中。此外，我们开发了一种方法，可以适当地从对两人中选择一个有助于提高性能的人。我们使用公共和私人数据集评估网络的性能，并确认具有重叠相机视图的域中的性能超过了最先进的方法。据我们所知，这是关于域自适应学习的首次研究，并具有在真实环境中可以获得的多相机约束。
Person re-identification is a key technology for analyzing video-based human behavior; however, its application is still challenging in practical situations due to the performance degradation for domains different from those in the training data. Here, we propose an environment-constrained adaptive network for reducing the domain gap. This network refines pseudo-labels estimated via a self-training scheme by imposing multi-camera constraints. The proposed method incorporates person-pair information without person identity labels obtained from the environment into the model training. In addition, we develop a method that appropriately selects a person from the pair that contributes to the performance improvement. We evaluate the performance of the network using public and private datasets and confirm the performance surpasses state-of-the-art methods in domains with overlapping camera views. To the best of our knowledge, this is the first study on domain-adaptive learning with multi-camera constraints that can be obtained in real environments.
