Abstract
Deep learning methods have started to dominate the research progress of video-based person re-identification (re-id). However, existing methods mostly consider supervised learning, which requires exhaustive manual efforts for labelling cross-view pairwise data. Therefore, they severely lack scalability and practicality in real-world video surveillance applications. In this work, to address the video person re-id task, we formulate a novel Deep Association Learning (DAL) scheme, the first end-to-end deep learning method using none of the identity labels in model initialisation and training. DAL learns a deep re-id matching model by jointly optimising two margin-based association losses in an end-to-end manner, which effectively constrains the association of each frame to the best-matched intra-camera representation and cross-camera representation. Existing standard CNNs can be readily employed within our DAL scheme. Experiment results demonstrate that our proposed DAL significantly outperforms current state-of-the-art unsupervised video person re-id methods on three benchmarks: PRID 2011, iLIDS-VID and MARS.
Abstract (translated)
深度学习方法已开始主导基于视频的人员重新识别（re-id）的研究进展。然而，现有方法主要考虑监督学习，其需要用于标记交叉视图成对数据的详尽手动努力。因此，它们在现实世界的视频监控应用中严重缺乏可扩展性和实用性。在这项工作中，为了解决视频人员重新识别任务，我们制定了一种新颖的深度关联学习（DAL）方案，这是第一种在模型初始化和训练中不使用任何身份标签的端到端深度学习方法。 DAL通过以端到端方式联合优化两个基于边缘的关联损失来学习深度重新匹配模型，这有效地约束了每个帧与最佳匹配的摄像机内表示和交叉摄像机表示的关联。现有的标准CNN可以在我们的DAL方案中容易地使用。实验结果表明，我们提出的DAL在三个基准测试中显着优于当前最先进的无监督视频人员重新识别方法：PRID 2011，iLIDS-VID和MARS。
URL
https://arxiv.org/abs/1808.07301
