LEAPS: End-to-End One-Step Person Search With Learnable Proposals
Zhiqiang Dong, Jiale Cao, Rao Muhammad Anwer, Jin Xie, Fahad Khan,, Yanwei Pang

TL;DR
LEAPS introduces an end-to-end person search method with learnable proposals, enabling direct detection and re-identification without post-processing, achieving state-of-the-art accuracy and speed on benchmark datasets.
Contribution
The paper presents a novel end-to-end person search framework with learnable proposals and a dynamic re-id head, improving accuracy and efficiency over existing methods.
Findings
Achieves 55.0% mAP on CUHK-SYSU, outperforming previous methods by 1.7%.
Provides around two-fold speedup on PRW dataset.
Eliminates the need for non-maximum suppression in person search.
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…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Human Mobility and Location-Based Analysis · IoT and GPS-based Vehicle Safety Systems
