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Journal of Electrical and Computer Engineering
Volume 2015 (2015), Article ID 706187, 7 pages
Research Article

Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features

College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China

Received 11 November 2014; Revised 28 May 2015; Accepted 31 May 2015

Academic Editor: Sethuraman Panchanathan

Copyright © 2015 Yaqin Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Smoke detection is a very key part of fire recognition in a forest fire surveillance video since the smoke produced by forest fires is visible much before the flames. The performance of smoke video detection algorithm is often influenced by some smoke-like objects such as heavy fog. This paper presents a novel forest fire smoke video detection based on spatiotemporal features and dynamic texture features. At first, Kalman filtering is used to segment candidate smoke regions. Then, candidate smoke region is divided into small blocks. Spatiotemporal energy feature of each block is extracted by computing the energy features of its 8-neighboring blocks in the current frame and its two adjacent frames. Flutter direction angle is computed by analyzing the centroid motion of the segmented regions in one candidate smoke video clip. Local Binary Motion Pattern (LBMP) is used to define dynamic texture features of smoke videos. Finally, smoke video is recognized by Adaboost algorithm. The experimental results show that the proposed method can effectively detect smoke image recorded from different scenes.