Abstract
Forest fires solely are responsible for a multitude of environmental disasters. This in turn results in economic and ecological/environmental damage, thus bringing peril to people’s lives. Automatic surveillance vehicles and early detection in forest fires have gained more importance in comparison to the traditional human monitoring. This is because human monitoring is subjective, which affects reliability in detection and is one of the major concerns in forest-fire detection systems. In the present system, the process is time-consuming and false alarms must be declared manually. This research is an attempt to solve the aforementioned problem. The hypothesis is to deploy a drone equipped with a thermal camera to capture the area on fire and locate victims and give the shortest distance to them. This process is entirely done when the firefighters are in transit to the location. The main focus of this text is thermal video processing since it plays a crucial role in the entire prototype. This paper highlights the difference between two human detection algorithms: Histogram of Oriented Gradients (HOG) [1] and You Only Look Once (YOLOv2) [2, 3] for thermal videos. Furthermore, an algorithm for detecting fire in a thermal video has also been implemented. The inputs are various thermal videos which are available publicly. The conclusion is that the HOG algorithm is faster than the YOLO algorithm for thermal inputs but the latter is more accurate.
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Tammana, A., Amogh, M.P., Gagan, B., Anuradha, M., Vanamala, H.R. (2021). Thermal Image Processing and Analysis for Surveillance UAVs. In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 190. Springer, Singapore. https://doi.org/10.1007/978-981-16-0882-7_50
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