This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.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 quantitativelyThis work was funded by Thales UK, the Centre of Excellence for
Sensor and Imaging System (CENSIS), and the Scottish Funding Council under the project
“AALART. Thales-Challenge Low-pixel Automatic Target Detection and Recognition (ATD/ATR)”,
ref. CAF-0036. Thanks are also given to the Digital Health and Care Institute (DHI, project
Smartcough-MacMasters), which partially supported Mr. Monge-Alvarez’s contribution, and
to the Royal Society of Edinburgh and National Science Foundation of China for the funding
associated to the project “Flood Detection and Monitoring using Hyperspectral Remote Sensing
from Unmanned Aerial Vehicles”, which partially covered Dr. Casaseca-de-la-Higuera’s,
Dr. Luo’s, and Prof. Wang’s contribution. Dr. Casaseca-de-la-Higuera would also like to acknowledge
the Royal Society of Edinburgh for the funding associated to project “HIVE”