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Research on Image Fire Detection Based on Support Vector Machine
In order to detect and alarm early fire timely and effectively, traditional temperature and smoke fire detectors are vulnerable to environmental factors such as the height of monitoring space, air velocity, dust. An image fire detection algorithm based on support vector machine is proposed by studying the features of fire in digital image. Firstly, the motion region is extracted by the inter-frame difference method and regarded as the Suspected fire area. Then, the uniform size is sampled again. Finally, the flame color moment feature and texture feature are extracted and input into the support vector machine for classification and recognition. Data sets were formed by collecting Internet resources and fire videos taken by oneself and the trained support vector machine was tested. The test results showed that the algorithm can detect early fire more accurately.
Research on Image Fire Detection Based on Support Vector Machine
In order to detect and alarm early fire timely and effectively, traditional temperature and smoke fire detectors are vulnerable to environmental factors such as the height of monitoring space, air velocity, dust. An image fire detection algorithm based on support vector machine is proposed by studying the features of fire in digital image. Firstly, the motion region is extracted by the inter-frame difference method and regarded as the Suspected fire area. Then, the uniform size is sampled again. Finally, the flame color moment feature and texture feature are extracted and input into the support vector machine for classification and recognition. Data sets were formed by collecting Internet resources and fire videos taken by oneself and the trained support vector machine was tested. The test results showed that the algorithm can detect early fire more accurately.
Research on Image Fire Detection Based on Support Vector Machine
Chen, Ke (author) / Cheng, Yanying (author) / Bai, Hui (author) / Mou, Chunjie (author) / Zhang, Yuchun (author)
2019-10-01
3194295 byte
Conference paper
Electronic Resource
English
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