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We propose an end-to-end one-step person search approach with learnableproposals, named LEAPS. Given a set of sparse and learnable proposals, LEAPSemploys a dynamic person search head to directly perform person detection andcorresponding re-id feature generation without non-maximum suppressionpost-processing. The dynamic person search head comprises a detection head anda novel flexible re-id head. Our flexible re-id head first employs a dynamicregion-of-interest (RoI) operation to extract discriminative RoI features ofthe proposals. Then, it generates re-id features using a plain and ahierarchical interaction re-id module. To better guide discriminative re-idfeature learning, we introduce a diverse re-id sample matching strategy,instead of bipartite matching in detection head. Comprehensive experimentsreveal the benefit of the proposed LEAPS, achieving a favorable performance ontwo public person search benchmarks: CUHK-SYSU and PRW. When using the sameResNet50 backbone, our LEAPS obtains a mAP score of 55.0%, outperforming thebest reported results in literature by 1.7%, while achieving around a two-foldspeedup on the challenging PRW dataset. Our source code and models will bereleased.

LEAPS: End-to-End One-Step Person Search With Learnable Proposals