## 1 Introduction  
Recent reports of the NFPA (National Fire Protection Association) show that the average number of fires per year is about 1.3 million in the US alone, with a high cost in terms of lives (more than 3000 civilian fire fatalities) and economic losses--the cost for fire losses is estimated to be about 55 billion US Dollars (USD) per year [1]. Hence, with the advent of the Internet of Things (IoT) and the growing interest about safety in public places, an early fire-smoke detection system should be implemented for the benefit of all citizens. To this end, a video-based approach based on the recognition of fire smoke is a promising method. Indeed, the video signal is a wide monitor of the area under investigation and often closed-circuit television (CCTV) systems for surveillance purpose are already installed in smart buildings, in public places in smart cities, or onboard passenger vehicles of public transport systems. Exploiting an already existing video infrastructure allows the reduction of purchase/installation costs of additional add-on products, increasing only the complexity of the algorithm used to detect the smoke.  
The smoke is the first event of a fire hazard since it appears before the flames. Standard smoke sensors, based on chemical, temperature, or PIR (Passive InfraRed) detectors [2; 3; 4; 5; 6; 7], trigger an alarm within several minutes when combustion is already producing flames and increasing the environmental temperature. In [8; 9], a photoelectric smoke detector with an actual smoke chamber was mixed with smoke temperature. Current standards such as EN50155 for onboard train safety set a delay between the start of fire and its detection of 1 min. This is why the commercial solution in [10] for onboard train antifire systems uses point-based optical and temperature smoke detectors. These sensors aim at revealing the presence of a fire with a time delay within 1 min. The system in [10] exploits the fact that hot air produced by a fire moves from the bottom to the top of the train wagon, and hence the smoke moves toward the sensor placed on the roof. In [11], flames were identified by measuring absolute temperature and its gradient through a smart IR sensor. The detection systems presented have the drawback of reacting slowly. Moreover, they need active fans or air-conditioning to speed-up the smoke/fire detection process, thus avoiding a too high measuring latency. Instead, the aim of this work is to develop an innovative video-based smoke detection technique able to trigger the alarm within a few seconds. Such an algorithm has been already implemented into several IoT embedded devices to develop a distributed antifire system accessible via web browser and able to signal a fire alarm from different camera nodes, discussed in [12]. With this paper, the authors intend to extend that discussion by focusing more on the smoke detection algorithm aspects.  
Hereafter, the paper is organized as follows. Section 2 deals with a detailed review of video-based smoke/fire measurements. Section 3 presents the new Advanced Video SmokE Detection (AdViSED) technique discussing the global architecture and then each of the algorithms used in the multiple video processing steps. Section 4 discusses the AdViSED thresholds configuration. Section 5 presents the evaluation of the complexity of the algorithm and the performance compared with the state of art systems. Implementation results of AdViSED in x86-based and ARM (Advanced RISC Machine)-based platforms are analyzed in Section 6. Conclusions are drawn in Section 7.