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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2023

13.06.2022

Global Context-Aware Feature Extraction and Visible Feature Enhancement for Occlusion-Invariant Pedestrian Detection in Crowded Scenes

verfasst von: Zhenxing Liu, Xiaoning Song, Zhenhua Feng, Tianyang Xu, Xiaojun Wu, Josef Kittler

Erschienen in: Neural Processing Letters | Ausgabe 1/2023

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Abstract

The research in pedestrian detection has made remarkable progress in recent years. However, robust pedestrian detection in crowded scenes remains a considerable challenge. Many methods resort to additional annotations (visible body or head) of a dataset or develop attention mechanisms to alleviate the difficulties posed by occlusions. However, these methods rarely use contextual information to strengthen the features extracted by a backbone network. The main aim of this paper is to extract more effective and discriminative features of pedestrians for robust pedestrian detection with heavy occlusions. To this end, we propose a Global Context-Aware module to exploit contextual information for pedestrian detection. Fusing global context with the information derived from the visible part of occluded pedestrians enhances feature representations. The experimental results obtained on two challenging benchmarks, CrowdHuman and CityPersons, demonstrate the effectiveness and merits of the proposed method. Code and models are available at: https://​github.​com/​FlyingZstar/​crowded-pedestrian-detection.
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Metadaten
Titel
Global Context-Aware Feature Extraction and Visible Feature Enhancement for Occlusion-Invariant Pedestrian Detection in Crowded Scenes
verfasst von
Zhenxing Liu
Xiaoning Song
Zhenhua Feng
Tianyang Xu
Xiaojun Wu
Josef Kittler
Publikationsdatum
13.06.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2023
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10910-w

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