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 prese...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/706187 |
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| _version_ | 1832558866633588736 |
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| author | Yaqin Zhao Zhong Zhou Mingming Xu |
| author_facet | Yaqin Zhao Zhong Zhou Mingming Xu |
| author_sort | Yaqin Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6d4d5dc4f4984934b024f7db32df55dc |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-6d4d5dc4f4984934b024f7db32df55dc2025-02-03T01:31:18ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552015-01-01201510.1155/2015/706187706187Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture FeaturesYaqin Zhao0Zhong Zhou1Mingming Xu2College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSmoke 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.http://dx.doi.org/10.1155/2015/706187 |
| spellingShingle | Yaqin Zhao Zhong Zhou Mingming Xu Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features Journal of Electrical and Computer Engineering |
| title | Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features |
| title_full | Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features |
| title_fullStr | Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features |
| title_full_unstemmed | Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features |
| title_short | Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features |
| title_sort | forest fire smoke video detection using spatiotemporal and dynamic texture features |
| url | http://dx.doi.org/10.1155/2015/706187 |
| work_keys_str_mv | AT yaqinzhao forestfiresmokevideodetectionusingspatiotemporalanddynamictexturefeatures AT zhongzhou forestfiresmokevideodetectionusingspatiotemporalanddynamictexturefeatures AT mingmingxu forestfiresmokevideodetectionusingspatiotemporalanddynamictexturefeatures |