Fire Detection in Video Stream by Using Simple Artificial Neural Network

  • Peter Janku Tomas Bata University in Zlin, Faculty of Applied Informatics
  • Zuzana Kominkova Oplatkova Tomas Bata University in Zlin, Faculty of Applied Informatics
  • Tomas Dulik Tomas Bata University in Zlin, Faculty of Applied Informatics
  • Petr Snopek UNIS a.s.
  • Jiri Liba UNIS a.s.
Keywords: Fire detection, Computer vision, Artificial neural networks


This paper deals with the preliminary research of the fire detection in a video stream. Early fire detection can save lives and properties from huge losses and damages. Therefore the surveillance of the areas is necessary. Early fire discovery with high accuracy, i.e. a low number of false positive or false negative cases, is essential in any environment, especially in places with the high motion of people. The traditional fire detection sensors have some drawbacks: they need separate systems and infrastructure to be implemented, to use sensors in the case of the industrial environment with open fire technologies is often impossible, and others. The fire detection in a video stream is one of the possible and feasible solutions suitable for replacement or supplement of conventional fire detection sensors without a need for installation a huge infrastructure. The paper provides the state of the art in the fire detection. The following part of the paper proposes the new system of feature extraction and describes the feedforward neural network which was used for the training and testing of the proposed idea. The promising results are presented with over 93% accuracy on a selected dataset of movies which consist of more and highly varied instances than published by other researchers involved in the fire detection field. The structure of the neural networks promises higher computational speed than currently implemented deep learning systems.


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How to Cite
Janku, P., Kominkova Oplatkova, Z., Dulik, T., Snopek, P. and Liba, J. 2018. Fire Detection in Video Stream by Using Simple Artificial Neural Network. MENDEL. 24, 2 (Dec. 2018), 55–60. DOI: