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Authors: Wen-Hui Chen ; Chi-Wei Kuan and Chuan-Cho Chiang

Affiliation: Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei, Taiwan

Keyword(s): Advanced Driver Assistance Systems, Convolutional Neural Networks, Deformable Part Models, Pedestrian Detection.

Abstract: Pedestrian detection has many real-world applications, such as advanced driver assistance systems, security surveillance, and traffic control, etc. One of the pedestrian detection challenges is the presence of occlusion. In this study, a jointly learned approach using multiscale deformable part models (DPM) and convolutional neural networks (CNN) is presented to improve the detection accuracy of partially occluded pedestrians. Deep convolutional networks provide a framework that allows hierarchical feature extraction. The DPM is used to characterize non-rigid objects on the histogram of oriented gradients (HoG) feature maps. Scores of the root and parts filters derived from the DPM are used as deformable information to help improve the detection performance. Experimental results show that the proposed jointly learned model can effectively reduce the miss rate of CNN-based object detection models tested on the Caltech pedestrian dataset.

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Paper citation in several formats:
Chen, W.; Kuan, C. and Chiang, C. (2020). Integrating Multiscale Deformable Part Models and Convolutional Networks for Pedestrian Detection. In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-419-0; ISSN 2184-495X, SciTePress, pages 515-521. DOI: 10.5220/0009459005150521

@conference{vehits20,
author={Wen{-}Hui Chen. and Chi{-}Wei Kuan. and Chuan{-}Cho Chiang.},
title={Integrating Multiscale Deformable Part Models and Convolutional Networks for Pedestrian Detection},
booktitle={Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2020},
pages={515-521},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009459005150521},
isbn={978-989-758-419-0},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Integrating Multiscale Deformable Part Models and Convolutional Networks for Pedestrian Detection
SN - 978-989-758-419-0
IS - 2184-495X
AU - Chen, W.
AU - Kuan, C.
AU - Chiang, C.
PY - 2020
SP - 515
EP - 521
DO - 10.5220/0009459005150521
PB - SciTePress

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