In recent years we have seen an upsurge in terror attacks around the world. Such attacks usually happen in public places with large crowds with the intention of causing the most damage possible and getting the most attention for their cause. Despite the fact that surveillance cameras are assumed to be a powerful tool, their effect in preventing crime is far from clear due to either limitations in humans' ability to vigilantly monitor video surveillance or for the simple reason that they are operating passively. This research will introduce a weapon detection system based on an ensemble of semantic Convolutional Neural Networks that decomposes the problem of detecting and locating a weapon into a set of smaller problems concerned with the individual component parts of a weapon.
Publications:
[1] Egiazarov, A., Mavroeidis, V., Zennaro, F. M., & Vishi, K. (2019, November). Firearm detection and segmentation using an ensemble of semantic neural networks. In 2019 European Intelligence and Security Informatics Conference (EISIC) (pp. 70-77). IEEE.