# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 2 Related Works  
As the conventional computer vision techniques, the traditional vision-based fire detection is decomposed into two parts: exploration of appropriate features and exploitation of them for fire decision. Colors of flame and smoke are important for identifying fire region in a still image even though they generally vulnerable to a variety of environmental factors [6; 7; 8; 9]. [PERSON] and [PERSON] [10] adopted the Bayes classifier with additional features, including area, surface, and boundary of the fire area. [PERSON] proposed to use optical flow, a simple type of short-term dynamic feature, for the fire area and the neural network-based decision method [11]. [PERSON] [12] proposed a multi-expert system for reliable decision that combined the results of an analysis of a fire's color, shape, and motion characteristics. Although they were insufficient to detect a fire on their own, supplementary features, such as texture, shape, and optical flow, were helpful to reduce false detections. [PERSON] et al. [13] modeled the fire behavior by employing various spatial-temporal features, such as color probability, flickering, spatial, and spatial-temporal energy. [PERSON] et al. [14] used several general volume local binary patterns to extract dynamic texture, including LBPTOP, VLBP, CLBPTOP, and CVLBP. Furthermore, dynamic texture descriptors were obtained using Weber Local Descriptor in Three Orthogonal Planes (WLD-TOP) [15]. All these conventional dynamic texture analyses require domain knowledge of fires on captured images. Moreover, almost all methods account for the short-term dynamic behavior of fire, while a fire has longer-term dynamic behavior. In recent years, deep learning has been successfully applied to diverse areas such as object detection/classification in images, and researchers have conducted several studies on fire detection to explore whether and how deep learning can improve performance [16; 17; 18; 19]. The DNN-based approach has several differences from conventional vision-based fire detection. Because the visual features of fire are automatically explored in the multi-layered convolutional neural network (CNN) by training, the effort to identify proper handcrafted features is unnecessary. In addition, the detector/classifier can be obtained by the training simultaneously in the same DNN. Therefore, the appropriate network structure becomes more important with an efficient training algorithm. In addition, DNN training requires a large amount of data for exploring features and determining classifier/detector that exploits them. [PERSON] [17] proposed a fire detection method based on CNN feature extraction, and followed by a multilayer perceptron (MLP)-type classifier. [PERSON] et al. [18] also proposed a CNN-based fire detection method operated in a cascaded fashion; at first, the global image-level fire classifier was used, then a fine-grained patch classifier for precisely localizing the fire. [PERSON] et al. also proposed a fine-tuned CNN fire detector [19], and recently developed an efficient CNN architecture for fire detection, localization, and semantic understanding of the scene of the fire [20]. [PERSON] et al. [21] exploited a simple region of interest (ROI) detection method using motion-flicker-based dynamic features to try to finely decide the ROI whether it is a fire or not by a CNN-based classifier. [PERSON] et al. [22] proposed a lightweight fire detection inspired by MobileNet. [PERSON] et al. [23] also adopted object detection models including Faster R-CNN, R-FCN, SSD, and YOLO v.3 for fire detection. Furthermore, [PERSON] et al. [24] proposed another lightweight real-time fire and smoke detection model, which they named Firenet. Even though DNN showed superior image-level fire classification performance against traditional computer vision approach, locating objects has been another problem as, for example, in [PERSON]'s cascade approach.