Abstract
In this day and age, we are witness to ever increasing gun violence all around the world. Technology has surpassed all human beliefs where each person can be easily tracked through their mobiles or through the fortitude of CCTV cameras available all across public properties and areas. There is a need to stop gun violence to protect people’s Right to Live. There are several instances appearing in the news daily about deaths caused due to gun violence. An alarm based system can be introduced which tracks the publicly available CCTV footage to look for guns in the open and raise appropriate alarms. In order to achieve this a robust model to identify and classify firearms automatically from videos is required. The aim of this paper is to describe a YOLO-based model which is highly effective in recognizing firearms in videos and mark them in the video such that the model can be further used for further applications such as raising alarms, tracking human beings with firearms etc.
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India sees the third-highest firearm-related deaths in the world by Joe Myers https://theprint.in/india/india-sees-the-third-highest-firearm-related-deaths-in-the-world/274576
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Mishra, S., Chaurasiya, V.K. (2023). Automatic Firearm Detection in Images and Videos Using YOLO-Based Model. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_46
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