Rethinking data collection for person re-identification: active redundancy reduction

•To address the data collection problem in person reID, we present a novel active redundancy reduction framework to alleviate the data redundancy problem in public re-ID datasets.•To minimize the annotation workload while maximizing the performance of the re-ID model, a simple baseline is presented to select informative and diverse samples for annotation by estimating their uncertainty and intra-diversity.•A computer-assisted Identity Recommendation Module is proposed to help the human annotators to rapidly and accurately label the selected samples.