loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Authors: Chloe Kim 1 ; Mahdi Oghaz 2 ; Jiri Fajtl 2 ; Vasileios Argyriou 2 and Paolo Remagnino 2

Affiliations: 1 VCA Technology Ltd, Surrey and U.K. ; 2 Kingston University, London and U.K.

Keyword(s): Embedded Systems, Deep Learning, Object Detection, Convolutional Neural Network, Person Detection, YOLO, SSD, RCNN, R-FCN.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Segmentation and Grouping

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 Res Net-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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.144.55.253

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - Volume 5: VISAPP, ISBN 978-989-758-354-4; ISSN 2184-4321, pages 459-465. DOI: 10.5220/0007386304590465

@conference{visapp19,
author={Chloe Kim. and Mahdi 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 - Volume 5: VISAPP,},
year={2019},
pages={459-465},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007386304590465},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - A Comparison of Embedded Deep Learning Methods for Person Detection
SN - 978-989-758-354-4
IS - 2184-4321
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

0123movie.net