# A Video-Based Fire Detection Using Deep Learning Models
## 1 Introduction  
Fire is an abnormal event which can quickly cause significant injury and property damage [1]. According to the National Fire Protection Association (NAPA), the United States fire department responded to an estimated 1,319,500 fires during 2017 [2], which resulted in 3,400 civilian fire fatalities, 14,670 civilian fire injuries, and an estimated $23 billion in direct property loss. In order to reduce such disasters, fire detection without a false alarm at an early stage is crucial. Accordingly, various automatic fire detection technologies are being developed, and are widely used in real life. In general, two broad categories of technologies can be identified: traditional fire alarm and fire detection by computer vision. Traditional fire alarm technology is based on smoke or heat sensors that require proximity for activation. These sensors need human involvement to confirm a fire in case of alarm. Furthermore, such systems require various equipment to provide information on the size, location, and burning degree of the fire. To overcome these limitations, researchers have been investigating computer vision-based methods combined with various types of supplementary sensors [3; 4; 5; 6]. This category of technologies gives larger surveillance coverage and offers the advantage of less human intervention with a faster response, as a fire can be confirmed without requiring a visit to the fire location, and provides detailed fire information such as location, size, and degree. Despite these advantages, however, some issues remain concerning the system complexity, and false detection according to diverse reasons. Therefore, researchers have invested significant effort to address these issues in terms of computer vision technology. Early research on computer vision-based fire detection was focused on the color of a fire within the framework of a rule-based system, which is often sensitive to environmental conditions suchas illumination and weather. So, further studies added supplementary features to the color of a fire, including area, surface, boundary, and motion of the suspected region, with other types of decision-making algorithms, such as Bayes classifier and multi-expert systems, in order to make a robust decision. Nevertheless, almost all the research tries to detect the flame and smoke in a single frame of Closed-Circuit Television (CCTV) or a limited number of frames in a short period. In general, it is not an easy task to explore the static and dynamic characteristics of diverse flame and smoke to be exploited in a vision system, as it requires a large amount of domain knowledge. In the deep learning approach however, these exploration and exploitation processes can be replaced by the training of an appropriate neural network with a sufficient amount of data in order to avoid overfitting. This approach, therefore, becomes convenient once a dataset with many flame and smoke images or video clips has been built. In this paper, we propose a deep-learning-based fire detection method, which imitates the human detection process and which we call the detection and temporal accumulations (DTA) for the fire decision. Usually, a human can detect a suspected fire object in a scene, continuously monitor it, and accumulate the temporal behaviors to finally decide whether it is a fire or not. We assume that this DTA process can greatly reduce erroneous fire detection. In the proposed method, the suspected region of fire (SRoF) is detected with its spatial features against non-fire objects by the Faster Region-based Convolutional Neural Network (R-CNN). Then, the features summarized from the object detection model in successive frames are accumulated by Long Short-Term Memory (LSTM) to classify whether there is a fire or not in a short-term period, which can be treated as a person's glance for fire detection. The decisions for successive short-term periods are then combined in the majority voting for the final decision in a long-term period.