Deep convolutional framework for abnormal behavior detection in a smart surveillance system
- Authors
- Ko, Kwang-Eun; Sim, Kwee-Bo
- Issue Date
- Jan-2018
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Behavior recognition; Convolutional neural network; Long short-term memory; Smart surveillance system
- Citation
- ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v.67, pp 226 - 234
- Pages
- 9
- Journal Title
- ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Volume
- 67
- Start Page
- 226
- End Page
- 234
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/1337
- DOI
- 10.1016/j.engappai.2017.10.001
- ISSN
- 0952-1976
1873-6769
- 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.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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