## 2 State of the Art Review of Video-Based Smoke Detection Algorithm  
Video-based fire measuring and warning systems may help to reduce the detection time compared to other available sensors (smoke chemicals or temperature sensors) in both indoor and outdoor scenarios. A video camera can monitor \"volumes\" without transport delays that traditional \"point\" sensors suffer from. Particularly, CCTV cameras are suitable for fire detection within 100 m, which is the case of fire onboard passenger vehicles or in houses, offices, factories, or cities. Instead, for wildfire in forests or rural environments [13], which is out of the scope of this work, other techniques must be used, optimized for scenes observed at distances of several km. Many studies have been recently proposed in literature for video smoke detection [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. In [24], a commercial solution was proposed exploiting both a \(160\times 120\)-pixel, 9 fps, long wave infrared camera for thermal imaging, and a \(1920\times 1080\)-pixel color CMOS (Complementary Metal Oxide Semiconductor) camera to exploit the thermal energy emitted from the objects and to analyze their dynamic features. Hence, absolute and gradient temperatures plus size and motion of the objects were analyzed by the system in [24]. However, the purchase and installation of such a solution is too expensive for exhaustive use in smart cities, homes/offices, and intelligent transport systems. In [25], a CCD (charge-coupled device) camera was used but only as a temperature sensor. The system in [25] was used to estimate the edges of a combustion flame and not as a fast fire/smoke detector. The authors in [13] implemented a background subtraction using frame differencing and working with the smoke features to discover the smoke areas. In the same direction is the work of [PERSON] and [PERSON], which in [14] generates the background subtraction using Visual Background Estimation (ViBe). The ViBe technique was used also in [15] in combination with the dissipation function to estimate the background. Another smoke recognition was studied in [16], combining fuzzy logic, Gaussian Mixed Model (GMM), and Support Vector Machine (SVM) for recognition. In [19], the background and the foreground were estimated with the use of the Kalman filter. The work in [20] proposed an early fire detection system using Internet Protocol (IP) camera technology with Motion JPEG codec with the Discrete Cosine Transform (DCT) operating in the frequency domain for smoke features. Instead, the work in [21] focused the algorithm on the appearance-based method and then a background subtraction was applied. In [26], edge detection image processing was used to reveal the edge of flames, rather than implementing a fire warning algorithm. In [27], a multiexpert decision algorithm was used for fire detection: combining the results of three expert systems exploiting motion, shape, and color information, which achieved a detection accuracy higher than 90% over the testing dataset. However, the complexity of the proposed technique in [27] limits the real-time algorithmic execution on a Raspberry Pi embedded system to only 3 fps and using only low-resolution images, 320 x 240 pixels. A convolutional neural networks (CNN) approach for video-based antifire surveillance systems was proposed in [18; 28], but also in this case the implementation complexity, although reduced compared to other CNN-based techniques, is still too high. For example, [28] required a system equipped with a NVidia GeForce GTX TITAN X with 12 GB onboard memory and deep learning framework and Intel Core i5 CPU with 64 GB RAM. Such a platform has a cost in the order of hundreds of USD with a power consumption of hundreds of Watts. A hybrid computing architecture combining a GPU and a general-purpose processor has also been adopted in [29]. Here, the algorithms exploited motion estimation based on background subtraction and color probability analysis to detect candidate smoke blobs.