Next Article in Journal
An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities
Previous Article in Journal
Molecularly Imprinted Polymer Nanoparticles for Formaldehyde Sensing with QCM
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(7), 1010;

Robust Behavior Recognition in Intelligent Surveillance Environments

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 16 May 2016 / Revised: 17 June 2016 / Accepted: 25 June 2016 / Published: 30 June 2016
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [7055 KB, uploaded 30 June 2016]   |  


Intelligent surveillance systems have been studied by many researchers. These systems should be operated in both daytime and nighttime, but objects are invisible in images captured by visible light camera during the night. Therefore, near infrared (NIR) cameras, thermal cameras (based on medium-wavelength infrared (MWIR), and long-wavelength infrared (LWIR) light) have been considered for usage during the nighttime as an alternative. Due to the usage during both daytime and nighttime, and the limitation of requiring an additional NIR illuminator (which should illuminate a wide area over a great distance) for NIR cameras during the nighttime, a dual system of visible light and thermal cameras is used in our research, and we propose a new behavior recognition in intelligent surveillance environments. Twelve datasets were compiled by collecting data in various environments, and they were used to obtain experimental results. The recognition accuracy of our method was found to be 97.6%, thereby confirming the ability of our method to outperform previous methods. View Full-Text
Keywords: intelligent surveillance system; visible light camera; thermal camera; behavior recognition intelligent surveillance system; visible light camera; thermal camera; behavior recognition

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Batchuluun, G.; Kim, Y.G.; Kim, J.H.; Hong, H.G.; Park, K.R. Robust Behavior Recognition in Intelligent Surveillance Environments. Sensors 2016, 16, 1010.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top