Neural Person Search Machines

Abstract

We investigate the problem of person search in the wild in this work. Instead of comparing the query against all candidate regions generated in a query-blind manner, we propose to recursively shrink the search area from the whole image till achieving precise localization of the target person, by fully exploiting information from the query and contextual cues in every recursive search step. We develop the Neural Person Search Machines (NPSM) to implement such recursive localization for person search. Benefiting from its neural search mechanism, NPSM is able to selectively shrink its focus from a loose region to a tighter one containing the target automatically. In this process, NPSM employs an internal primitive memory component to memorize the query representation which modulates the attention and augments its robustness to other distracting regions. Evaluations on two benchmark datasets, CUHK-SYSU Person Search dataset and PRW dataset, have demonstrated that our method can outperform current state-of-the-arts in both mAP and top-1 evaluation protocols.

arXiv commentICCV2017 camera ready

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Cited by in corpus (9)

• AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
• SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification
• Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification
• Re-ID done right: towards good practices for person re-identification
• Person Search via A Mask-Guided Two-Stream CNN Model
• Person Search by Multi-Scale Matching
• Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search
• SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification
• Cross-Resolution Person Re-identification with Deep Antithetical Learning