Résumé
Person Re-Identification (Re-ID) is receiving a lot of attention. Large datasets containing labeled images of various individuals have been released, and successful approaches were developed. However, when Re-ID models are deployed in new cities or environments, they face an important domain shift (ethnicity, clothing, weather, architecture, etc.), resulting in decreased performance. In addition, the whole frames of the video streams must be converted into cropped images of people using pedestrian detection models, which behave differently from the human annotators who built the training dataset. To better understand the extent of this issue, this paper introduces a complete methodology to evaluate Re-ID approaches and training datasets with respect to their suitability for unsupervised deployment for live operations. We benchmark four Re-ID approaches on three datasets, providing insight and guidelines that can help to design better Re-ID pipelines.