Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features

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. copyright © 2015 yaqin zhao et al.