## 7 Conclusions  
The paper proposes AdViSED, a novel video smoke detection algorithm for antifire surveillance systems, considering both outdoor and indoor application scenarios. To reduce installations costs, the application scenario considers a fixed single camera, working in the visible spectral range, already installed in a close circuit television system for surveillance purpose. Thanks to the adoption of a Kalman-based motion detection technique, color analysis, image segmentation, blob labeling, time/edge-based bloc analysis, geometrical features analysis, and M out of N decay, the measurement system is able to generate an alarm signal with improved estimation performance compared with the state-of-art techniques in terms of improved response latency and measurement metrics. The latter are calculated in terms of F1, accuracy precision, recall, and MCC metrics. For example, when compared to [22; 23] with a set of video tests, available from the Firesense EU project, the accuracy and precision metrics are improved by about 20%, the MCC score doubles, and the recall increases up to 1. The computational complexity of the proposed technique is reduced by 63% compared with the work in [22; 23], when considering the same hardware and software computing platform. With respect to the works in [20; 36; 40; 41; 42], AdViSED ensures a reduced response latency, while achieving equal or better measurement accuracy metrics. Several tests, carried out with different frame rate and frame size, have confirmed the scalability of the proposed measurement techniques to different input camera sensors. AdViSED has been implemented in platforms using both x86 64b GPP processors or embedded ones, based on ARM cores, such as the Raspberry Pi 3. The latter achieves in real-time a performance eight times better than state-of-art works targeting the same embedded unit [27]. Power measurements of the embedded implementation prove that its power cost is below 2.4 W. The low cost and power of the final implementation platform, which also includes Wi-Fi and Bluetooth connection capabilities, make it suitable for the implementation of a distributed measuring systems for smoke/fire surveillance in application scenarios like smart cities or intelligent transport systems. As a future step to improve the camera-based antifire surveillance system we will try adopting some lightweight deep learning networks like MobileNet, ShuffleNet, and SqueezeNet [32; 33; 34; 35]. Although these deep neural nets are not directly related to smoke detection, a possible transfer learning solution could be used in order to improve the algorithm used as the base of the proposed distributed antifire and surveillance system [12].  
[PERSON] and [PERSON] conceived and designed the experiments, performed the experiments, analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported by Crosslab IoT-Dipartimenti di Eccellenza project by University of Pisa/MIUR and by POR FSE EFEST project (Tuscany Region and Solari di Udine S.P.A.).  
Discussions with [PERSON] for power measurements, [PERSON] for embedded system porting, and [PERSON] for EFEST support are gratefully acknowledged.  
The authors declare no conflict of interest.