A novel infrared video surveillance system using deep learning based techniques
Zhang, H; Luo, C; Wang, Q; et al.Kitchin, M; Parmley, A; Monge-Alvarez, J; Casaseca-de-la-Higuera, P
Date: 11 April 2018
Journal
Multimedia Tools and Applications
Publisher
Springer Verlag (Germany)
Publisher DOI
Abstract
This paper presents a new, practical infrared video based surveillance
system, consisting of a resolution-enhanced, automatic target detection/recognition
(ATD/R) system that is widely applicable in civilian and military applications. To
deal with the issue of small numbers of pixel on target in the developed ATD/R
system, as are ...
This paper presents a new, practical infrared video based surveillance
system, consisting of a resolution-enhanced, automatic target detection/recognition
(ATD/R) system that is widely applicable in civilian and military applications. To
deal with the issue of small numbers of pixel on target in the developed ATD/R
system, as are encountered in long range imagery, a super-resolution method is
employed to increase target signature resolution and optimise the baseline quality
of inputs for object recognition. To tackle the challenge of detecting extremely
low-resolution targets, we train a sophisticated and powerful convolutional neural
network (CNN) based faster-RCNN using long wave infrared imagery datasets
that were prepared and marked in-house. The system was tested under different
weather conditions, using two datasets featuring target types comprising pedestrians
and 6 different types of ground vehicles. The developed ATD/R system can
detect extremely low-resolution targets with superior performance by effectively
addressing the low small number of pixels on target, encountered in long range applications.
A comparison with traditional methods confirms this superiority both
qualitatively and quantitatively
Computer Science
Faculty of Environment, Science and Economy
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