Paper Abstract and Keywords |
Presentation |
2022-03-07 13:50
[Poster Presentation]
Head location estimation using CSRNet for understanding Highly Congested Scenes Takuya Nagatoshi, Michiharu Niimi (KIT) EMM2021-95 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Neural network for congested scene recognition called CSRNet has been proposed. The purpose of CSRNet is to count the number of heads based on the output of CSRNet. We plane to apply CSRNet to the attendance management system which is able to recognize who are attending in a classroom, in order to do that, we need to estimate head location. In CSRNet, the input is an RGB image that is taken for a scene, and the output is the density map of people. The number of heads is given by the integral of density map. The learning process of CSRNet is performed by decreasing the mean square error between the output and the correct density map. Note that the size of density map is 1/8 of input image. Because the density map can be regarded as 2-dimentinal signal, we can estimate head locations by determining its maximal value. In this report, we put an up-sampling layer inside of CSRNet to try to make a detailed density map. The size of density map become 1/4 of input image. We apply gaussian filters to density map to reduce the influence of noise. In the experiments, we used ShanghaiTech data set and real pictures with is taken at a real classroom. As a result of experiments, we confirmed that the effectiveness of adding up-sampling layer, but it is difficult to adjust head location error and multiple extraction. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
congested scene recognition / head location estimation / CSRNet / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 417, EMM2021-95, pp. 17-22, March 2022. |
Paper # |
EMM2021-95 |
Date of Issue |
2022-02-28 (EMM) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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EMM2021-95 |
Conference Information |
Committee |
EMM |
Conference Date |
2022-03-07 - 2022-03-08 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
(Primary: Online, Secondary: On-site) |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Image and Sound Quality, Metrics for Perception and Recognition, Human Auditory and Visual System, etc. |
Paper Information |
Registration To |
EMM |
Conference Code |
2022-03-EMM |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Head location estimation using CSRNet for understanding Highly Congested Scenes |
Sub Title (in English) |
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congested scene recognition |
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head location estimation |
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CSRNet |
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1st Author's Name |
Takuya Nagatoshi |
1st Author's Affiliation |
Kyushu Institute of Technology (KIT) |
2nd Author's Name |
Michiharu Niimi |
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Kyushu Institute of Technology (KIT) |
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Speaker |
Author-1 |
Date Time |
2022-03-07 13:50:00 |
Presentation Time |
15 minutes |
Registration for |
EMM |
Paper # |
EMM2021-95 |
Volume (vol) |
vol.121 |
Number (no) |
no.417 |
Page |
pp.17-22 |
#Pages |
6 |
Date of Issue |
2022-02-28 (EMM) |
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