A Comparison of Embedded Deep Learning Methods for Person Detection

Chloe Eunhyang Kim, Mahdi Maktab Dar Oghaz, Jiri Fajtl, Vasileios Argyriou, Paolo Remagnino

2019

Abstract

Recent advancements in parallel computing, GPU technology and deep learning provide a new platform for complex image processing tasks such as person detection to flourish. Person detection is fundamental preliminary operation for several high level computer vision tasks. One industry that can significantly benefit from person detection is retail. In recent years, various studies attempt to find an optimal solution for person detection using neural networks and deep learning. This study conducts a comparison among the state of the art deep learning base object detector with the focus on person detection performance in indoor environments. Performance of various implementations of YOLO, SSD, RCNN, R-FCN and SqueezeDet have been assessed using our in-house proprietary dataset which consists of over 10 thousands indoor images captured form shopping malls, retails and stores. Experimental results indicate that, Tiny YOLO-416 and SSD (VGG-300) are the fastest and Faster-RCNN (Inception ResNet-v2) and R-FCN (ResNet-101) are the most accurate detectors investigated in this study. Further analysis shows that YOLO v3-416 delivers relatively accurate result in a reasonable amount of time, which makes it an ideal model for person detection in embedded platforms.

Paper Citation

in Harvard Style

Kim C., Oghaz M., Fajtl J., Argyriou V. and Remagnino P. (2019). A Comparison of Embedded Deep Learning Methods for Person Detection. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 459-465. DOI: 10.5220/0007386304590465

in Bibtex Style

@conference{visapp19,
author={Chloe Eunhyang Kim and Mahdi Maktab Dar Oghaz and Jiri Fajtl and Vasileios Argyriou and Paolo Remagnino},
title={A Comparison of Embedded Deep Learning Methods for Person Detection},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={459-465},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007386304590465},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - A Comparison of Embedded Deep Learning Methods for Person Detection
SN - 978-989-758-354-4
AU - Kim C.
AU - Oghaz M.
AU - Fajtl J.
AU - Argyriou V.
AU - Remagnino P.
PY - 2019
SP - 459
EP - 465
DO - 10.5220/0007386304590465
PB - SciTePress