LEAPS: End-to-End One-Step Person Search With Learnable Proposals
- URL: http://arxiv.org/abs/2303.11859v1
- Date: Tue, 21 Mar 2023 13:59:32 GMT
- Title: LEAPS: End-to-End One-Step Person Search With Learnable Proposals
- Authors: Zhiqiang Dong, Jiale Cao, Rao Muhammad Anwer, Jin Xie, Fahad Khan,
Yanwei Pang
- Abstract summary: We propose an end-to-end one-step person search approach with learnable proposals, named LEAPS.
Given a set of sparse and learnable proposals, LEAPS employs a dynamic person search head to directly perform person detection and corresponding re-id feature generation without non-maximum suppression post-processing.
- Score: 50.39493100627476
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose an end-to-end one-step person search approach with learnable
proposals, named LEAPS. Given a set of sparse and learnable proposals, LEAPS
employs a dynamic person search head to directly perform person detection and
corresponding re-id feature generation without non-maximum suppression
post-processing. The dynamic person search head comprises a detection head and
a novel flexible re-id head. Our flexible re-id head first employs a dynamic
region-of-interest (RoI) operation to extract discriminative RoI features of
the proposals. Then, it generates re-id features using a plain and a
hierarchical interaction re-id module. To better guide discriminative re-id
feature learning, we introduce a diverse re-id sample matching strategy,
instead of bipartite matching in detection head. Comprehensive experiments
reveal the benefit of the proposed LEAPS, achieving a favorable performance on
two public person search benchmarks: CUHK-SYSU and PRW. When using the same
ResNet50 backbone, our LEAPS obtains a mAP score of 55.0%, outperforming the
best reported results in literature by 1.7%, while achieving around a two-fold
speedup on the challenging PRW dataset. Our source code and models will be
released.
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