Abstract
Video-based smoke detection is an effective method for fire alarm systems. Given the widespread use of high-definition cameras, a smoke detection method for high-definition video is needed. This paper proposes a smoke-detection framework for high-definition video, in which the main idea is to use the small smoke image blocks to match the image features of the motion area in the video and to use the support vector machine classifier for smoke recognition. The ViBe algorithm and other methods are used to effectively extract the areas for classification. This detection framework consists of spatial- and frequency-domain features. In the extraction of frequency domain features, we use local phase quantization (LPQ) features. In the local texture features of the spatial domain, we add the compensation of adjacent pixels and consider the gradient of the symmetrical pixels using the center-symmetric local binary pattern feature. To improve results, we also propose the trisection feature fusion scheme for features in the spatial and frequency domains. The experiments show that using the feature extraction and fusion schemes, our smoke-detection framework achieves the good performance in the detection of smoke in the video from different datasets.
Highlights
Fire is one of the major disasters that seriously endanger the safety of human life and property
CENTER-SYMMETRIC Local binary patterns (LBP) The LBP operator is a grayscale texture descriptor proposed by Ojala et al [13] that can capture the spatial characteristics of images
When the CSGC-LBP and local phase quantization (LPQ) algorithms are using the trisection feature fusion scheme, we achieve the best classification performance in both data sets, which indicates that this classifier can be applied to smoke detection
Summary
Fire is one of the major disasters that seriously endanger the safety of human life and property. The main process of a video smoke detection algorithm is to determine candidate smoke areas, and use a classifier to classify static features such as wavelets directly extracted from the candidate smoke area to determine the final smoke detection result. Z. Liu et al.: Smoke-Detection Framework for High-Definition Video extract the texture features of smoke. In [12], a video-based smoke-detection method using dynamic texture feature extraction with volume LBPs was proposed. CENTER-SYMMETRIC LBP The LBP operator is a grayscale texture descriptor proposed by Ojala et al [13] that can capture the spatial characteristics of images. It has strong classification ability and high computational efficiency.
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