Pose-aware multi-feature fusion network for driver distraction recognition

Mingyan Wu, Xi Zhang, Linlin Shen, Hang Yu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Traffic accidents caused by distracted driving have gradually increased in recent years. In this work, we propose a novel multi-feature fusion network based on pose estimation, for image based distracted driving detection. Since hand is the most important part of driver to infer the distracted actions, our proposed method firstly detects hands using the human body posture information. In addition to the features extracted from the whole image, our network also include the important information of hand and human body posture. The global feature, hand and pose features are finally fused by weighted combination of probability vectors and concatenation of feature maps. The experimental results show that our method achieves state-of-the-art performance on our own SZ Bus Driver dataset and the public AUC Distracted Driver dataset.

Original languageEnglish
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1228-1235
Number of pages8
ISBN (Electronic)9781728188089
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: 10 Jan 202115 Jan 2021

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference25th International Conference on Pattern Recognition, ICPR 2020
Country/TerritoryItaly
CityVirtual, Milan
Period10/01/2115/01/21

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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