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Person re-identification (ReID) remains a very difficult challenge incomputer vision, and critical for large-scale video surveillance scenarioswhere an individual could appear in different camera views at different times.There has been recent interest in tackling this challenge using cross-domainapproaches, which leverages data from source domains that are different thanthe target domain. Such approaches are more practical for real-world widespreaddeployment given that they don't require on-site training (as with unsupervisedor domain transfer approaches) or on-site manual annotation and training (aswith supervised approaches). In this study, we take a systematic approach toestablishing a large baseline source domain and target domain for cross-domainperson ReID. We accomplish this by conducting a comprehensive analysis to studythe similarities between source domains proposed in literature, and studyingthe effects of incrementally increasing the size of the source domain. Thisallows us to establish a balanced source domain and target domain split thatpromotes variety in both source and target domains. Furthermore, using lessonslearned from the state-of-the-art supervised person re-identification methods,we establish a strong baseline method for cross-domain person ReID. Experimentsshow that a source domain composed of two of the largest person ReID domains(SYSU and MSMT) performs well across six commonly-used target domains.Furthermore, we show that, surprisingly, two of the recent commonly-useddomains (PRID and GRID) have too few query images to provide meaningfulinsights. As such, based on our findings, we propose the following balancedbaseline for cross-domain person ReID consisting of: i) a fixed multi-sourcedomain consisting of SYSU, MSMT, Airport and 3DPeS, and ii) a multi-targetdomain consisting of Market-1501, DukeMTMC-reID, CUHK03, PRID, GRID and VIPeR.

Fairest of Them All: Establishing a Strong Baseline for Cross-Domain Person ReID