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Recently, Person Re-Identification (Re-ID) has received a lot of attention.Large datasets containing labeled images of various individuals have beenreleased, allowing researchers to develop and test many successful approaches.However, when such Re-ID models are deployed in new cities or environments, thetask of searching for people within a network of security cameras is likely toface an important domain shift, thus resulting in decreased performance.Indeed, while most public datasets were collected in a limited geographic area,images from a new city present different features (e.g., people's ethnicity andclothing style, weather, architecture, etc.). In addition, the whole frames ofthe video streams must be converted into cropped images of people usingpedestrian detection models, which behave differently from the human annotatorswho created the dataset used for training. To better understand the extent ofthis issue, this paper introduces a complete methodology to evaluate Re-IDapproaches and training datasets with respect to their suitability forunsupervised deployment for live operations. This method is used to benchmarkfour Re-ID approaches on three datasets, providing insight and guidelines thatcan help to design better Re-ID pipelines in the future.

Benchmarking person re-identification datasets and approaches for practical real-world implementations