Early Fire Detection Using HEP and Space-time Analysis
This work addresses fire safety by improving smoke detection for early warning systems, but it is incremental as it builds on existing texture and space-time analysis methods.
The authors tackled early fire detection by developing a video-based alarm system that monitors smoke, using Histograms of Equivalent Patterns (HEP) to evaluate texture features and proposing Block-based Inter-Frame Difference (BIFD) with an improved LBP-TOP for space-time analysis. Their method achieved better accuracy and fewer false alarms compared to state-of-the-art technologies, as shown in SVM-based experiments.
In this article, a video base early fire alarm system is developed by monitoring the smoke in the scene. There are two major contributions in this work. First, to find the best texture feature for smoke detection, a general framework, named Histograms of Equivalent Patterns (HEP), is adopted to achieve an extensive evaluation of various kinds of texture features. Second, the \emph{Block based Inter-Frame Difference} (BIFD) and a improved version of LBP-TOP are proposed and ensembled to describe the space-time characteristics of the smoke. In order to reduce the false alarms, the Smoke History Image (SHI) is utilized to register the recent classification results of candidate smoke blocks. Experimental results using SVM show that the proposed method can achieve better accuracy and less false alarm compared with the state-of-the-art technologies.