Deep convolutional framework for abnormal behavior detection in a smart surveillance system
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ko, Kwang-Eun | - |
dc.contributor.author | Sim, Kwee-Bo | - |
dc.date.available | 2019-01-22T14:15:33Z | - |
dc.date.issued | 2018-01 | - |
dc.identifier.issn | 0952-1976 | - |
dc.identifier.issn | 1873-6769 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/1337 | - |
dc.description.abstract | The ability to instantly detect risky behavior in video surveillance systems is a critical issue in a smart surveillance system. In this paper, a unified framework based on a deep convolutional framework is proposed to detect abnormal human behavior from a standard RGB image. The objective of the unified structure is to improve detection speed while maintaining recognition accuracy. The deep convolutional framework consists of (1) a human subject detection and discrimination module that is proposed to solve the problem of separating object entities, in contrast to previous object detection algorithms, (2) a posture classification module to extract spatial features of abnormal behavior, and (3) an abnormal behavior detection module based on long short-term memory (LSTM). Experiments on a benchmark dataset evaluate the potential of the proposed method in the context of smart surveillance. The results indicate that the proposed method provides satisfactory performance in detecting abnormal behavior in a real-world scenario. (C) 2017 Elsevier Ltd. All rights reserved. | - |
dc.format.extent | 9 | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | Deep convolutional framework for abnormal behavior detection in a smart surveillance system | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.engappai.2017.10.001 | - |
dc.identifier.bibliographicCitation | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v.67, pp 226 - 234 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000417656900018 | - |
dc.identifier.scopusid | 2-s2.0-85032504854 | - |
dc.citation.endPage | 234 | - |
dc.citation.startPage | 226 | - |
dc.citation.title | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | - |
dc.citation.volume | 67 | - |
dc.type.docType | Article | - |
dc.publisher.location | 영국 | - |
dc.subject.keywordAuthor | Behavior recognition | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.subject.keywordAuthor | Long short-term memory | - |
dc.subject.keywordAuthor | Smart surveillance system | - |
dc.subject.keywordPlus | ACTION RECOGNITION | - |
dc.subject.keywordPlus | MOTION CAPTURE | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.