# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 1 Introduction  
Fire is an atypical event that can cause significant injury and property damage over a very short time [1]. According to the National Fire Protection Association (NAPA), in 2017, the United States fire departments responded to an estimated 1,319,500 fires [2], which were responsible for 3400 civilian fatalities, 14,670 civilian injuries, and an estimated $23 billion in direct property loss. To reduce the number and degree of these disasters, fire detection that reliably avoids false alarms and misdetection at an early stage is crucial. To meet this need, a variety of automatic fire detection technologies are widely deployed, with new technologies under development. The fire detection technology falls into two broad categories: the traditional fire alarm and fire detection using computer vision. Traditional fire alarm technology relies on proximity-activated smoke or heat sensors. The sensors require human involvement to confirm a fire once the alarm sounds. These systems also require additional equipment to determine the size, location, and severity of the fire. To overcome these limitations, researchers have been investigating the possibilities of computer vision-based methods in combination with various supplementary sensors [3; 4; 5; 6; 7]. This category of technologies provides more comprehensive surveillance, and allows for less human intervention and faster responses (as a fire can be confirmed without requiring a visit to the fire location), and provides detailed fire information (including location, size, and severity). Despitethese advantages, issues with system complexity and false detection have stymied the development of these systems. As a result, researchers have devoted a great deal of effort to address these issues to computer vision technology. Early research on computer vision-based fire detection was focused on fire color, which varies depending on environmental conditions such as ambient light and weather. Additional studies looked for supplemental features in addition to color, including area, surface, boundary, and motion of and within the suspected region, applying various decision-making algorithms (including Bayes classifier and multi-expert system) to reach a trustworthy decision. With few exceptions, however, researchers have focused on flame and smoke detection from either a single frame or a small number of frames of closed-circuit television (CCTV). Aside from the static and dynamic characteristics of fire that manifest over a short period, flame and smoke display long-term dynamic behaviors. These dynamic behaviors, when identified by their distinct motion characteristics (such as the optical flow), extracted from consecutive frames of a video sequence, and combined with analysis of static characteristics, can be exploited to improve fire detection. However, it is not an easy task to explore the static and dynamic characteristics of flame and smoke, and exploit them in a vision-based system, as doing so requires a large amount of domain knowledge. By using a deep learning approach, however, exploration and exploitation can be performed by a properly trained neural network. This approach becomes feasible once a sufficient dataset of flame and smoke images and video clips has been built. The paper proposes a deep learning-based fire detection method, known as detection and temporal accumulations (DTA), which imitates the human fire detection process [3]. In DTA, the suspected region of fire (SRoF) is detected by the Faster Region-based Convolutional Neural Network (R-CNN) using the fire's spatial features as they appear against non-fire objects. Next, features summarized by the object detection model across successive frames are accumulated by long short-term memory (LSTM), which classifies images based on whether there is a fire or not across a short-term period. The decisions for successive short-term periods are then combined in the majority voting for the decision in a long-term period.