Abstract
With recent technological developments, criminal investigation has witnessed a revolutionary change in identifying crimes. This has empowered Law Enforcement Agencies (LEAs) to take benefit of such revolution and build a smart criminal investigation ecosystem. Generally, LEAs collect data through surveillance systems (e.g., cameras); which are implemented on public places in order to recognize people behaviors and visually identify those who may form any danger or risk. In this paper, we focus on knives-related crimes or attacks that have been increased in recent years. In order to ensure public safety, it is crucial to detect such type of attacks in an accurate and efficient way in order to help LEAs in reducing potential consequences. We propose a smart video surveillance system (SVSS), which is based on a modified Single Shot Detector (SSD) and is combined with InceptionV2 and MobileNetV2 models. The proposed system is believed to enable LEAs to analyze big data collected from sensor cameras in a real-time and to accurately detect knives-based attacks. Experimental result show that SVSS can achieve better results in real-life scenario in terms of obtaining rapid and accurate attack warnings.
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References
Castillo, A., Tabik, S., Pérez, F., Olmos, R., Herrera, F.: Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning. Neurocomputing 330, 151–161 (2019)
The United Nations Office on Drugs and Crime’s: Global study on homicide 2019: executive summary (2019)
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)
Fernandez-Carrobles, M.M., Deniz, O., Maroto, F.: Gun and knife detection based on faster R-CNN for video surveillance. In: Morales, A., Fierrez, J., Sánchez, J.S., Ribeiro, B. (eds.) IbPRIA 2019. LNCS, vol. 11868, pp. 441–452. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31321-0_38
Glowacz, A., Kmieć, M., Dziech, A.: Visual detection of knives in security applications using active appearance models. Multimedia Tools Appl. 74(12), 4253–4267 (2015)
Grega, M., Matiolański, A., Guzik, P., Leszczuk, M.: Automated detection of firearms and knives in a CCTV image. Sensors 16(1), 47 (2016)
Guo, R., Zhang, L., Ying, Y., Sun, H., Han, Y., Tan, H.: Automatic detection and identification of controlled knives based on improved SSD model. In: 2019 Chinese Automation Congress (CAC), pp. 5120–5125. IEEE (2019)
Huang, J., et al.: TensorFlow object detection API, Code. https://github.com/tensorflow/models.git. Documentation. https://modelzoo.co/model/objectdetection
Huang, J., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7310–7311 (2017)
Jiang, S., Qin, H., Zhang, B., Zheng, J.: Optimized loss functions for object detection and application on nighttime vehicle detection. arXiv preprint arXiv:2011.05523 (2020)
Kmieć, M., Glowacz, A.: Object detection in security applications using dominant edge directions. Pattern Recogn. Lett. 52, 72–79 (2015)
Kundegorski, M.E., Akçay, S., Devereux, M., Mouton, A., Breckon, T.P.: On using feature descriptors as visual words for object detection within x-ray baggage security screening. In: 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016), pp. 1–6. IEEE (2016)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Navalgund, U.V., Priyadharshini, K.: Crime intention detection system using deep learning. In: 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET), pp. 1–6. IEEE (2018)
Noever, D.A., Noever, S.E.M.: Knife and threat detectors. arXiv preprint arXiv:2004.03366, pp. 1–8 (2020)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Sarkar, D., Bali, R., Ghosh, T.: Hands-On Transfer Learning with Python: Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras. Packt Publishing Ltd. (2018)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
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Abdallah, R., Benbernou, S., Taher, Y., Younas, M., Haque, R. (2023). A Smart Video Surveillance System for Helping Law Enforcement Agencies in Detecting Knife Related Crimes. In: Awan, I., Younas, M., Bentahar, J., Benbernou, S. (eds) The International Conference on Deep Learning, Big Data and Blockchain (DBB 2022). DBB 2022. Lecture Notes in Networks and Systems, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-031-16035-6_6
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