# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 1 Introduction
Specifically, they considered entropy, contrast, the second angular momentum, difference inverse of the moment and the correlation among them in four different directions together with an Artificial Neural Network (ANN) classifier. A weakness of this scheme is that it only examines the texture, which may produce false positives and negatives due to the complexity of the description of the dynamics of the smoke texture. [PERSON] et al. [6] used GLCM with fourteen statistical measures describing smoke texture. To determine the movement with a difference of 1-s delay between images, they described the texture region with movement and used support vector machines to establish whether the texture corresponds to smoke. If the texture corresponds to smoke, the resulting regions are accumulated and, if a given threshold is exceeded, an alarm is activated. [PERSON] et al. [7] detected the presence of smoke using video sequences captured by IP cameras, in which some important characteristics of smoke, such as the properties of color, movement and growth, are used. The Discrete Cosine Transform (DCT) is used to increase the detection accuracy without requiring the inverse DCT operation. This system provides detection results with a false negative error rate of 4% and a false positive rate of 2%. [PERSON] et al. [8] introduced a hardware solution using different sensors such as CO, smoke and temperature sensors. [PERSON] et al. [9] used a camera and different characteristics such as texture, wavelet, and color edge orientation and proposed an AdaBoost-like classifier. The aim of the algorithms mentioned above is to avoid confusing the properties of smoke with other objects in the scene that contain some similar features. However, different situations complicate smoke detection causing false alarms, which still must be resolved. To solve some of these problems, this paper proposes a smoke detection scheme using some particular properties and characteristics of smoke. The proposed system employs a mix of smoke features such as motion, color and texture, which are processed in cascade, to detect the presence of smoke in the scene. For motion detection, the proposed algorithm combines the accumulation of movement by differentiating successive frames subtracting the adaptable background. The color detection is determined by using two rules that define shades of gray smoke. The texture characterization is performed by Local Binary Pattern (LBP) and Local Binary Pattern Variance (LBPV) [10] using a multiscale analysis. Next, the regions that meet these three characteristics are considered as candidate regions with smoke, which are ultimately tested to determine if the expansion through the temporal blocks overlap. The system was evaluated using standard videos, activating the alarm in an average of 6 s in videos containing smoke. The proposed system provides a detection rate of about 98%. The rest of the paper is organized as follows. Section 2 describes the overall scheme consisting of motion detection, color detection, texture classification analysis, growth analysis, direction analysis and alarm activation. In Section 3, the results are presented and discussed,. Finally, the paper concludes in Section 4.