Open-world Person Re-Identification by Multi-Label Assignment Inference

Person re-identification methods have recently made tremendous progress on maximizing re-identification accuracy between camera pairs. However, this line of work mostly shares an critical limitation it assumes re-identification in a ‘closed world’. That is, between a known set of people who all appear in both views of a single pair of cameras. This is clearly far from a realistic application scenario. In this study, we take a significant step toward a more realistic ‘open world’ scenario. We consider associating persons observed in more than two cameras where: multiple within-camera detections are possible; different people can transit between different cameras – so that there is only partial and unknown overlap of identity between people observed by each camera; and the total number of unique people among all cameras is itself unknown. To address this significantly more challenging open world scenario, we propose a novel framework based on online Conditional Random Field (CRF) inference. Experiments demonstrate the robustness of our approach in contrast to the limitations of conventional approaches in the open world context.