Wildfire smoke detection based on local extremal region segmentation and surveillance |
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| Affiliation: | 1. St. Michael''s College, One Winooski Dr., Colchester, VT 05439, USA;2. School of Forest Resources and Climate Change Institute, University of Maine, Orono, ME, USA;3. School of Environment, The University of Auckland, New Zealand;4. Surface Water Quality Bureau, Santa, Fe, NM, USA;1. Department of Ornamental Horticulture, School of Landscape Architecture, Zhejiang Agriculture and Forestry University, 311300 Lin’an Hangzhou, China;2. Nurturing Station for State Key Laboratory of Subtropical Silviculture, 311300 Lin’an Hangzhou, China;1. Key Laboratory for Silviculture and Conservation of Ministry of Education, School of Forestry, Beijing Forestry University, Beijing 100083, People’s Republic of China;2. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, People’s Republic of China;3. Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, People’s Republic of China;1. School of Information Engineering, Zhejiang Agriculture and Forestry University, Hangzhou, China;2. USDA Forest Service Forest Products Laboratory, Madison, WI, USA;3. USDA Forest Service, Northern Research Station, Princeton, WV, USA;1. Poznan University of Life Sciences, Faculty of Wood Technology, Department of Furniture Design, Wojska Polskiego 38/42, 60-637 Poznan, Poland;2. Gazi University, Faculty of Technology, Department of Wood Product Industry Engineering, 06500 Teknikokullar/Besevler/Ankara, Turkey;1. Department of Plant Sciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway;2. Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway;3. NIBIO, Norwegian Institute for Bioeconomy Research, NO-1431 Ås, Norway |
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| Abstract: | A novel video-based method is proposed for long-distance wildfire smoke detection. Since the long-distance wildfire smoke usually moves slowly and lacks salient features in the video, the detection is still a challenging problem. Unlike many traditional video-based methods that usually rely on the smoke color or motion for initial smoke region segmentation, we use the Maximally Stable Extremal Region (MSER) detection method to extract local extremal regions of the smoke. This makes the initial segmentation of possible smoke region less dependent on the motion and color information. Potential smoke regions are then selected from all the possible regions by using some static visual features of the smoke, helping to eliminate the non-smoke regions as many as possible. Once a potential smoke region is found, we keep tracking it by searching the best-matched extremal regions in the subsequent frames. At the same time, the propagating motions of the potential smoke region are monitored based on a novel cumulated region approach, which can be effectively used to identify the distinctive expanding and rising motions of smoke. This approach can also make the final smoke motion identification insensitive to image shaking. It was proved that the proposed method is able to reliably detect the long-distance wildfire smoke and simultaneously produce very few false alarms in actual applications. |
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| Keywords: | Wildfire smoke detection Maximally stable extremal region (MSER) Region tracking Smoke motion |
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