# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
## 1 Introduction  
Fire has played a significant role in promoting the progress of human civilization. However, without proper management, it is also one of the major disasters causing huge loss of human lives and property all over the world. Therefore, it is essential to propose a reliable and effective algorithm to detect and raise the alarm for fire as soon as possible. Traditional detection algorithms usually use heat sensors, thermocouples, or ionization sensors to detect fire, through detecting temperature and smoke particles [1]. In the past few years, Siemens launched a FDT221 fire detector. It is equipped with two redundant heat sensors, which monitor rooms in which a temperature rise is expected in case of fire. Microm also launched the MIX-200 series of intelligent sensors for residential applications, equipped with photoelectric smoke detectors and electronic thermistors. Although these sensors may work efficiently in some particular cases, they suffer from large propagation delays of smoke and temperature, resulting in the increase of fire detection latency. Other algorithms, based on beam or aspirated smoke detectors, have been used to attempt to reduce the detection latency. Still, they cannot solve the practical problem completely [2]. Moreover, all of the abovementioned sensors require being in close proximity to the fire. Optical sensors have the advantages of long detection distance and fast response. However, as point detectors, theirdetection area is limited. These traditional algorithms do not detect the fire itself directly, therefore they are not always reliable. Fire image detection is a relative novel technology based on video cameras, detecting the fire through the intelligent analysis of images with advanced algorithms. Compared with traditional sensors, the advantages of video cameras are listed as follows. First of all, video cameras can avoid detection latency to a great extent. They can also monitor a larger area as volume detectors, and are adaptable to outdoor locations, where traditional sensors are difficult to place. Finally, with the increasing concern of security, more and more surveillance cameras have been installed for various security applications, providing a convenient way to embed fire image detection into existing systems. Research on fire image detection mainly focuses on two missions: feature extraction and state detection. For the former, there have been several studies highlighting a set of features for fire detection, such as color, flickering, motion, and edge blurring. Among them, color is considered as one of the most important clues. [PERSON] et al. [3] developed computer algorithms to segment the fire zone in the HSV (hue, saturation, value) color space and discover the correspondence points of image pairs, deciding the location, size, and propagation of the fire. A method based on the RGB (red, green, blue) color model and the difference between two consecutive potential fire masks was proposed in Reference [4], which indicated whether the potential region was a fire or fire-like object. The other color models such as YCbCr and CIE Lab were also used for fire pixel classification [5; 6]. The analysis of fire-colored moving pixels in consecutive frames contributes to raising the alarm. Furthermore, some extra clues such as spatial difference and temporal changes are also involved. [PERSON] et al. [7] used the color, spatial, and temporal information and divided the video into spatiotemporal blocks, extracting covariance-based features from these blocks to detect fire. [PERSON] et al. [8] presented a probabilistic model for color-based fire detection to extract the features of candidate fire regions, including area size, boundary roughness, skewness, and surface coarseness. Unfortunately, color information and their derived descriptors are not reliable enough to perform fire detection. Due to the poor robustness in handling false alarm sources, they are affected easily by fire-colored objects, such as red flags, setting sun, lighting, and so on.