Abstract
In this paper we present our work towards an effective solution for detection of dangerous objects, such as firearms or knives in a Closed Circuit Television System. We have gathered a large, manually annotated dataset of recordings supplemented by our original artificial sample generation method. We have used this dataset for training of a convolutional neural network. We present our approach and training results. We have also implemented and present software architecture that implements the neural network. We have shown, that the convolutional neural networks are well suited even for such complex object detection task, when provided with enough training samples.
This work was supported by the Polish National Center for Research and Development under the LIDER Grant (No. LIDER/354/L-6/14/NCBR/2015).
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Donath, P., Grega, M., Guzik, P., Król, J., Matiolański, A., Rusek, K. (2020). Dangerous Tool Detection for CCTV Systems. In: Dziech, A., Mees, W., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2020. Communications in Computer and Information Science, vol 1284. Springer, Cham. https://doi.org/10.1007/978-3-030-59000-0_18
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