1 Introduction
Person re-identification (re-id) aims to re-identify individuals across multi-camera surveillance systems. Over the past few years, person re-id has received increasing attention due to its great potential in many real-world applications, such as searching for suspects or lost people. In addition, it is also a fundamental research topic in computer vision and pattern recognition. In a typical person re-id pipeline, the system is provided with a target person as probe and aims to search through a gallery of known ID records to find a match. Usually, the probe and the gallery consist of human detection results or manually annotated bounding boxes, as shown in Fig. 1a. The major challenges in person re-id include distinguishing different people sharing a similar appearance (e.g., wearing similar clothes), or, conversely, retrieving the same person that undergoes significant appearance changes (due to pose/viewpoint variations, different illumination conditions, and occlusions). Although sophisticated deep learning architectures and metric learning schemes have greatly improved the individual representation capability, the conventional setting for person re-id neglects to take into account the fact that people are likely to walk in groups in real scenarios.