Abstract
The detection of real-time object has drawn an increased interest in surveillance strategies, and it is one of the applications of CNNs. This project has focussed on the detection of fire and pistols in places that are tracked by cameras and fires in the home, business explosions, and wildfires are all major headaches that have negative implications for the environment. Mass shooting and violence caused by guns are also on the upward push in certain components of the sector. Such type of incidents is time touchy and may purpose a huge loss to lifestyles and assets. Hence, the proposed system is designed with YOLO v3 that detects perfectly by analysing the video or live feed frame to frame to discover such type of situations in real time and send an alert to the authorities through an email and mobile message. The model has been working well on data sets like IMFDB and Fire Net with accuracy more than 83%. Experimental output satisfies the aim of the proposed design is implemented on Raspberry Pi and that is tested with unique situations, and its detection is also very fast, and it can be installed indoor and outdoor.
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Acknowledgements
We sincerely thank the faculty of Department of Electronics and Communication Engineering, Vardhaman College of Engineering, Hyderabad for their help in reviewing of the manuscript and constructive suggestions made at various levels of the research work and thank the management for providing the facilities to carry out our research work at #3021 Lab physically/remotely (Centre of Excellence for IoT).
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Chaithanya, J.K., Alisha, M., Sagar, S.M., Raghuram, K. (2022). Development of Real-Time Violence Detection with Raspberry Pi . In: Bindhu, V., Tavares, J.M.R.S., Du, KL. (eds) Proceedings of Third International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-16-8862-1_5
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