Impact statement: Our method can help to identify and track different types of objects ( e . g ., vehicles, cyclists, pedestrians, etc . ) across different cameras (domains), thus boosting the development of smart retail, smart transportation, and smart security systems in the future metropolises. In addition, our proposed self-paced contrastive learning is quite general and not limited to the specific research field of object re-ID. It can be well extended to broader research areas, including unsupervised and semi-supervised representation learning. However, object re-ID systems, when applied to identify pedestrians and vehicles in surveillance systems, might give rise to the infringement of people’s privacy, since such re-ID systems often rely on non-consensual surveillance data for training, i . e ., it is unlikely that all human subjects even knew they were being recorded. Therefore, governments and officials need to carefully establish strict regulations and laws to control the usage of re-ID technologies. Otherwise, re-ID technologies can potentially equip malicious actors with the ability to surveil pedestrians or vehicles through multiple CCTV cameras without their consent. The research committee should also avoid using the datasets with ethics issues, e . g ., DukeMTMC [37], which has been taken down due to the violation of data collection terms, should no longer be used. We would not evaluate our method on DukeMTMC related benchmarks as well. Furthermore, we should be cautious of the misidentification of the re-ID systems to avoid possible disturbance. Also, note that the demographic makeup of the datasets used is not representative of the broader population.