A novel infrared video surveillance system using deep learning based techniques

Huaizhong Zhang, Chunbo Luo, Qi Wang, Matthew Kitchin, Andrew Parmley, Jesus Monge-Alvarez, Pablo Casaseca-de-la-Higuera

Research output: Contribution to journalArticle (journal)peer-review

33 Citations (Scopus)
92 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)26657-26676
Number of pages20
JournalMultimedia Tools and Applications
Early online date11 Apr 2018
Publication statusPublished - 1 Oct 2018


  • ATD/R
  • CNN
  • Object detection
  • Super-resolution
  • Video surveillance


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