# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 1 Introduction  
Earlier, faster and accurate fire detection is very important to safeguard human lives, wildlife, forests, etc., because it allows a timely response by emergency services. Smoke, which is the first symptom in flameless combustion in most fires, has various important characteristics that can be used for early fire detection: low temperature, movement, gray tones, dynamic texture, and ascending expansion. Thus, intelligent and automated video surveillance systems, which have been a topic of active research and development during the last years, exploit those qualities for early fire detection. These systems have important advantages over traditional systems, such as quick detection, not requiring smoke contact, can operate in open spaces or adverse air currents, remote sensing capabilities, and take advantage of the existing infrastructure in many buildings and public areas. These systems are based on visual fire detection techniques that use standard video surveillancecameras, together with sophisticated computational analysis algorithms that segment the images to describe and recognize the visual fire characteristics to determine their presence. Numerous computer vision algorithms for fire detection have been proposed, which can be classified into two broad categories: flame-based and smoke-based video detection algorithms. In both cases, the primarily extracted features are the movement and color together with a mixture of additional parameters such as texture, expansion, silhouette, blinking, direction, transparency, changes in geometry, and self-similarity, among others. In most fires, the smoke appears before the flames, because the expanding movement increases the probability of falling into the visual field of the camera. Thus, this work focuses on the detection of smoke to activate the fire alarm. Several algorithms have been reported in the literature regarding early fire detection. [PERSON] et al. [1] proposed an early fire warning, based on video processing using two decision rules: a static rule-based decision and a dynamic decision rule. Here, the static decision rule follows the chromaticity of gray-colored smoke, which states that, in the Red-Green-Blue (RGB) color space, the three components must have very similar values, while the dynamic decision rules depend on the attributes of smoke spread, where silhouette disorder is analyzed against one circumference. It can be considered as a disadvantage since there are several objects whose relationship with the circumference may be similar to smoke. [PERSON] [2] proposed a model of cumulative movement based on the integral image by estimating the orientation based of fast-moving smoke, however, this estimation is not accurate enough because it is based on the sum of the blocks. To solve this limitation, an accumulation of orientation in time is performed, which offsets the results of the inaccuracy of the orientation, considering also the movement, color and direction. Then, to determine the movement, the differentiation of successive images is used. In the color components, it applies the rules proposed in [1] and adds an extra rule that expands to bluish shades detection. A drawback of this scheme is that it considers only the analysis of the upward movement of the smoke, which is not always the case. [PERSON] et al. [3,4] proposed a fire detection scheme using the characteristics of semi-transparency and the changing shape of smoke over time. It is based on the fact that the edges of an object covered by smoke lose their sharpness, resulting in a decrease of high frequencies. The authors analyzed the images with wavelet transform and monitored the slow and gradual reduction of wavelet components using hidden Markov models with a temporary wavelet analysis. This can be considered as a flaw because the processing time is considerable, which complicates its implementation in real time [3,4]. [PERSON] et al. [5] analyzed only the texture and proposed a combined use of wavelets and Gray Level Co-occurrence Matrix (GLCM) with four parameters.