Person re-identification (Re-ID) aims to match person images across non-overlapping camera views. The majority of Re-ID methods focus on small-scale surveillance systems in which each pedestrian is captured in different camera views of adjacent scenes. However, in large-scale surveillance systems that cover larger areas, it is required to track a pedestrian of interest across distant scenes (e.g., a criminal suspect escapes from one city to another). Since most pedestrians appear in limited local areas, it is difficult to collect training data with cross-camera pairs of the same person. In this work, we study intra-camera supervised person re-identification across distant scenes (ICS-DS Re-ID), which uses cross-camera unpaired data with intra-camera identity labels for training. It is challenging as cross-camera paired data plays a crucial role for learning camera-invariant features in most existing Re-ID methods. To learn camera-invariant representation from cross-camera unpaired training data, we propose a cross-camera feature prediction method to mine cross-camera self supervision information from camera-specific feature distribution by transforming fake cross-camera positive feature pairs and minimize the distances of the fake pairs. Furthermore, we automatically localize and extract local-level feature by a transformer. Joint learning of global-level and local-level features forms a global-local cross-camera feature prediction scheme for mining fine-grained cross-camera self supervision information. Finally, cross-camera self supervision and intra-camera supervision are aggregated in a framework. The experiments are conducted in the ICS-DS setting on Market-SCT, Duke-SCT and MSMT17-SCT datasets. The evaluation results demonstrate the superiority of our method, which gains significant improvements of 15.4 Rank-1 and 22.3 mAP on Market-SCT as compared to the second best method.

中文翻译：

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跨远景摄像机内监督人重识别的跨摄像机特征预测

人员重新识别 (Re-ID) 旨在匹配非重叠相机视图中的人员图像。大多数 Re-ID 方法专注于小规模监控系统，其中每个行人都在相邻场景的不同摄像机视图中被捕获。然而，在覆盖更大区域的大规模监控系统中，需要在远处的场景中跟踪感兴趣的行人（例如，犯罪嫌疑人从一个城市逃到另一个城市）。由于大多数行人出现在有限的局部区域，因此很难收集同一个人的跨摄像机对的训练数据。在这项工作中，我们研究了跨远景的相机内监督人员重新识别（ICS-DS Re-ID），它使用带有相机内身份标签的跨相机未配对数据进行训练。这是具有挑战性的，因为跨相机配对数据在大多数现有 Re-ID 方法中对于学习相机不变特征起着至关重要的作用。为了从跨摄像头未配对训练数据中学习摄像头不变表示，我们提出了一种跨摄像头特征预测方法，通过转换假的跨摄像头正特征对并最小化距离，从特定于摄像头的特征分布中挖掘跨摄像头自我监督信息假对。此外，我们通过转换器自动定位和提取局部特征。全局和局部特征的联合学习形成全局-局部跨相机特征预测方案，用于挖掘细粒度的跨相机自监督信息。最后，跨相机自我监督和相机内监督聚合在一个框架中。实验是在 ICS-DS 设置中在 Market-SCT、Duke-SCT 和 MSMT17-SCT 数据集上进行的。评估结果证明了我们的方法的优越性，与第二好的方法相比，在 Market-SCT 上获得了 15.4 Rank-1 和 22.3 mAP 的显着改进。