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Fire Technology

, Volume 50, Issue 3, pp 745–752 | Cite as

An Improved Probabilistic Approach for Fire Detection in Videos

  • Zhijie Zhang
  • Tian Shen
  • Jianhua Zou
Article

Abstract

This paper proposes an improved probabilistic approach using two improved feature representations. These features are color and motion. First, an improved probabilistic model for color-based fire detection is proposed, and candidate fire regions are generated from this model. Then, an improved motion feature is used for final decision. The performance of the proposed approach showed about 0.2758 accuracy in false positive rate, and 0.2636 accuracy in false negative rate on a benchmark fire video database, which represents a decrease of 46.6% in false positive rate, and a decrease of 52.1% in false negative rate compared to the probabilistic approach.

Keywords

Fire detection Probabilistic pattern recognition Color modeling Motion detection 

References

  1. 1.
    Hou J, Qian J, Zhang W, Zhao Z, Pan P (2011) Fire detection algorithms for video images of large space structures. Multimed Tools Appl 52:45–63CrossRefGoogle Scholar
  2. 2.
    Wang SJ, Jeng DL, Tsai MT (2009) Early fire detection method in video for vessels. J Syst Softw 82:656–667CrossRefGoogle Scholar
  3. 3.
    Gottuk DT, Lynch JA, Rose-Pehrsson SL, Owrutsky JC, Williams FW (2006) Video image fire detection for shipboard use. Fire Saf J 41:321–326CrossRefGoogle Scholar
  4. 4.
    Borges PVK, Izquierdo E (2010) A probabilistic approach for vision-based fire detection in videos. IEEE Trans Circuits Syst Video Technol 20(5):721–731CrossRefGoogle Scholar
  5. 5.
    Yuan F (2010) An integrated fire detection and suppression system based on widely available video surveillance. Mach Vis Appl 21:941–948CrossRefGoogle Scholar
  6. 6.
    Celik T, Demirel H, Ozkaramanli H, Uyguroglu M (2007) Fire detection using statistical color model in video sequences. J Vis Commun Image Represent 18(2):176–185CrossRefGoogle Scholar
  7. 7.
    Toreyin BU, Dedeoglu Y, Gudukbay U, Cetin AE (2006) Computer vision based method for real-time fire and flame detection. Pattern Recognit Lett 27:49–58CrossRefGoogle Scholar
  8. 8.
    Gunay O, Tasdemir K, Toreyin BU, Cetin AE (2010) Fire detection in video using LMS based active learning. Fire Technol 46:551–577CrossRefGoogle Scholar
  9. 9.
    Celik T (2010) Fast and efficient method for fire detection using image processing. ETRI J 32(6):881–890CrossRefMathSciNetGoogle Scholar
  10. 10.
    Celik T, Demirel H (2009) Fire detection in video sequences using a generic color model. Fire Saf J 44:147–158CrossRefGoogle Scholar
  11. 11.
    Marbach G, Loepfe M, Brupbacher T (2006) An image processing technique for fire detection in video images. Fire Saf J 41:285–289CrossRefGoogle Scholar
  12. 12.
    Schultze T, Kempka T, Willms I (2006) Audio–video fire-detection of open fires. Fire Saf J 41:311–314CrossRefGoogle Scholar
  13. 13.
    Fire detection sample video clips [DB/OL] (2012) http://signal.ee.bilkent.edu.tr/VisiFire/

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Systems Engineering InstituteXi’an Jiaotong UniversityXi’anPeople’s Republic of China

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