Paper Abstract and Keywords |
Presentation |
2012-09-20 12:50
Huge Flow Detection in Crowded Scenes using Dependent Dirichlet Process HMM Takuya Okamoto, Katsuya Kondo (Tottori Univ.) SIS2012-20 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this report, we present the framework of huge flow detection in crowded scenes. The flow analysis is done by using Dependent Dirichlet Process Hidden Markov Model (DDP-HMM) that can automate the clustering of time-varying trajectory of moving objects in complex dynamic scenes. Different motions that exist in the huge flows are detected. In addition, we calculate the degree of congestion in the scenes. We analyze the huge flows in crowded scenes by using optical flows and DDP-HMM on the scenes, and we detect the global flow and the motions of each moving object in the scene. In the global flow, moving objects in different direction exist locally. Therefore, we detect the different motions by comparing these flows. Moreover, the congestion degree is computed based on scene analysis and optical flows. The experimental results show that the huge flow can be analyzed, and the different motion and the congestion degree can be obtained in crowded scenes, where it is generally difficult to analyze. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Flow Analysis / Crowded Scenes / Hierarchical Dirichlet Process / Dependent Dirichlet Process HMM / Congestion Degree / / / |
Reference Info. |
IEICE Tech. Rep., vol. 112, no. 207, SIS2012-20, pp. 23-28, Sept. 2012. |
Paper # |
SIS2012-20 |
Date of Issue |
2012-09-13 (SIS) |
ISSN |
Print edition: ISSN 0913-5685 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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SIS2012-20 |
Conference Information |
Committee |
SIS IPSJ-AVM |
Conference Date |
2012-09-20 - 2012-09-21 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tottori Pref. Osaka Office |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
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Paper Information |
Registration To |
SIS |
Conference Code |
2012-09-SIS-AVM |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Huge Flow Detection in Crowded Scenes using Dependent Dirichlet Process HMM |
Sub Title (in English) |
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Keyword(1) |
Flow Analysis |
Keyword(2) |
Crowded Scenes |
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Hierarchical Dirichlet Process |
Keyword(4) |
Dependent Dirichlet Process HMM |
Keyword(5) |
Congestion Degree |
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1st Author's Name |
Takuya Okamoto |
1st Author's Affiliation |
Tottori University (Tottori Univ.) |
2nd Author's Name |
Katsuya Kondo |
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Tottori University (Tottori Univ.) |
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Speaker |
Author-1 |
Date Time |
2012-09-20 12:50:00 |
Presentation Time |
20 minutes |
Registration for |
SIS |
Paper # |
SIS2012-20 |
Volume (vol) |
vol.112 |
Number (no) |
no.207 |
Page |
pp.23-28 |
#Pages |
6 |
Date of Issue |
2012-09-13 (SIS) |
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