Localization Guided Learning for Pedestrian Attribute Recognition

Pedestrian attribute recognition has attracted many attentions due to its wide applications in scene understanding and person analysis from surveillance videos. Existing methods try to use additional pose, part or viewpoint information to complement the global feature representation for attribute cl...

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Bibliographic Details
Published inarXiv.org
Main Authors Liu, Pengze, Liu, Xihui, Yan, Junjie, Shao, Jing
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.08.2018
Subjects
Feature extraction
Localization
Proposals
Recognition
Scene analysis
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Summary:Pedestrian attribute recognition has attracted many attentions due to its wide applications in scene understanding and person analysis from surveillance videos. Existing methods try to use additional pose, part or viewpoint information to complement the global feature representation for attribute classification. However, these methods face difficulties in localizing the areas corresponding to different attributes. To address this problem, we propose a novel Localization Guided Network which assigns attribute-specific weights to local features based on the affinity between proposals pre-extracted proposals and attribute locations. The advantage of our model is that our local features are learned automatically for each attribute and emphasized by the interaction with global features. We demonstrate the effectiveness of our Localization Guided Network on two pedestrian attribute benchmarks (PA-100K and RAP). Our result surpasses the previous state-of-the-art in all five metrics on both datasets.
ISSN:2331-8422