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Visual spatial-context based wildfire smoke sensor


Sensors for early fire detection based on visual analysis have been under constant development and improvement, especially during the last decade. However, there is still a lot of room for advancement to increase the accuracy and reliability of such sensors. In this paper, a novel method for wildfire smoke detection based on spatial context analysis as well as motion detection, chromatic, texture and shape analysis is introduced. Several measures for evaluating quality of smoke detection are used, both on image and pixel scale. Smoke is a semi-transparent and amorphous phenomenon whose boundaries are hard to determine precisely; therefore, fuzzy measures are introduced for assessing the detection error. The proposed method is evaluated using the proposed measures and compared with two existing methods. The results show that the wildfire sensor based on proposed method is capable of detecting fire-smoke accurately and reliably, and in most detection aspects it outperforms the existing methods.

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Ministry of Science, Education and Sport of the Republic of Croatia has supported this research under Grant 023-0232005-2003 “AgISEco Agent-based intelligent environmental monitoring and protection systems”. Testing images and video sequences could be found on our Wildfire Observers and Smoke Recognition Homepage These video sequences are partly collected by iForestFire monitoring units (

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Correspondence to Toni Jakovčević.

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Jakovčević, T., Stipaničev, D. & Krstinić, D. Visual spatial-context based wildfire smoke sensor. Machine Vision and Applications 24, 707–719 (2013).

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  • Wildfire smoke detection
  • Forest fire smoke detection
  • Visual context
  • Fuzzy evaluation measures