A Real-Time Fire Detection Method from Video with Multifeature Fusion

The threat to people's lives and property posed by fires has become increasingly serious. To address the problem of a high false alarm rate in traditional fire detection, an innovative detection method based on multifeature fusion of flame is proposed. First, we combined the motion detection and color detection of the flame as the fire preprocessing stage. This method saves a lot of computation time in screening the fire candidate pixels. Second, although the flame is irregular, it has a certain similarity in the sequence of the image. According to this feature, a novel algorithm of flame centroid stabilization based on spatiotemporal relation is proposed, and we calculated the centroid of the flame region of each frame of the image and added the temporal information to obtain the spatiotemporal information of the flame centroid. Then, we extracted features including spatial variability, shape variability, and area variability of the flame to improve the accuracy of recognition. Finally, we used support vector machine for training, completed the analysis of candidate fire images, and achieved automatic fire monitoring. Experimental results showed that the proposed method could improve the accuracy and reduce the false alarm rate compared with a state-of-the-art technique. The method can be applied to real-time camera monitoring systems, such as home security, forest fire alarms, and commercial monitoring.

火灾对人民生命财产造成的威胁日益严重。为解决传统火灾探测中误报率高的问题，提出了一种基于火焰多特征融合的创新探测方法。首先，我们将火焰的运动检测和颜色检测相结合，作为火灾预处理阶段。这种方法在筛选火灾候选像素时节省了大量计算时间。其次，尽管火焰是不规则的，但在图像序列中具有一定的相似性。基于这一特征，提出了一种基于时空关系的火焰质心稳定新算法，我们计算图像每一帧火焰区域的质心，并添加时间信息以获得火焰质心的时空信息。然后，我们提取包括火焰的空间变异性、形状变异性和面积变异性在内的特征，以提高识别的准确性。最后，我们使用支持向量机进行训练，完成对候选火灾图像的分析，实现火灾自动监测。实验结果表明，与现有技术相比，所提出的方法能够提高准确性并降低误报率。该方法可应用于实时摄像头监测系统，如家庭安防、森林火灾报警和商业监测等。