Abstract
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.
Highlights
Fires are a constant threat to forest ecological systems and human safety; forest fires are an important problem in regions which present hot climate
Methods for detecting fire video can be categorized as flame detection and smoke detection
Most of the fire video systems are mainly designed for smoke detection, since the appearance of smoke is in most cases more visible than the fire itself
Summary
Fires are a constant threat to forest ecological systems and human safety; forest fires are an important problem in regions which present hot climate. Smoke detection algorithms are divided into systems based on single and based on multiple frames. Smoke images are recognized by color [1, 2], texture [3, 4], and energy [5]. Smoke features are extracted by video sequence. Smoke detection systems have made some achievements but cannot be used as a self-sufficient solution. They often have a high false rate and need an additional human confirmation for final decision. For bringing the performance of the detection systems closer to the results that could currently be obtained by human observers, this paper presents a novel forest fire smoke video detection based on spatiotemporal energy and dynamic texture features.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
