Multiclass Support Vector Data Description With Dynamic Time Warping Kernel for Monitoring Fires in Diverse Nonfire Environments

On a global scale, fire disasters present a serious risk to human well-being and the natural surroundings and infrastructure. Traditional fire detection methods encounter a challenge of data imbalance, with an abundance of non-fire scenario data but limited availability from actual fire incidents, resulting in challenges in building an accurate model capable of detecting various types of fires. Moreover, in real-life situations, sensor data collected from the non-fire scenario may exhibit patterns that fall into more than one class. This specific scenario type, known as nuisance scenarios (e.g., smoking and cooking), shares some characteristics with fire scenarios but is considered non-fire, and neglecting such cases can result in delayed fire detection or trigger nuisance alarms. To address this issue, this paper proposes a novel fire monitoring system based on a multi-class support vector data description with a dynamic time warping kernel, which considers distinct sensor patterns from multiple non-fire scenarios. The developed system is able to effectively detect different fire types without prior knowledge by constructing distinct decision boundaries for different non-fire classes. In addition, the proposed system further considers the temporal dynamic and shape patterns present within sensor signals by incorporating the dynamic time warping kernel function. Experimental results demonstrate the proposed system’s superiority over existing methods in detecting different fire types earlier and more accurately with lower false alarm rates in non-fire scenarios.