Detection of weapon possession and fire in Public Safety surveillance cameras
Resumo
The employment of video surveillance cameras by public safety agencies enables incident detection in monitored cities by using object detection for scene description, enhancing the protection to the general public. Object detection has its drawbacks, such as false positives. Our work aims to enhance object detection and image classification by employing IoU (Intersection over Union) to minimize the false positives and identify weapon holders or fire in a frame, adding more information to the scene.
Referências
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University, G. (2019). What is Public Safety and Where Do You Fit in? [link]. [Online; Last accessed 15 Jun 2021].
Wilson, B., Hoffman, J., and Morgenstern, J. (2019). Predictive inequity in object detection. CoRR, abs/1902.11097.
Zhang, A., Lipton, Z. C., Li, M., and Smola, A. J. (2021). Dive into deep learning. arXiv preprint arXiv:2106.11342.
BRASIL (2021). Constituição da república federativa do brasil de 1988. [link]. [Online; Last accessed 10 Aug 2021].
Cazzolato, M., Avalhais, L., Chino, D., Ramos, J., Souza, J., Rodrigues Jr, J., and Taina,
A. (2017). Fismo: A compilation of datasets from emergency situations for fire and smoke analysis.
Dey, N., Mishra, G., Kar, J., Chakraborty, S., and Nath, S. (2014). A survey of image classification methods and techniques.
Eletronica, R. (2021). Cidade mais monitorada do brasil, palotina reduz taxa de criminalidade em 80%. https://revistasegurancaeletronica.com.br. [Online; Last accessed 10 Aug 2021].
F., C. (2017). Deep Learning with Python. Manning Publications Co.
Fernández-Carrobles, M., Deniz, O., and Maroto, F. (2019). Gun and knife detection based on faster r-cnn for video surveillance.
Gelana, F. and Yadav, A. (2019). Firearm detection from surveillance cameras using image processing and machine learning techniques: Proceedings of icsiccs-2018.
González, J. L., Zaccaro, C., Alvarez-Garcia, J., Soria Morillo, L., and Caparrini, F. (2020). Real-time gun detection in cctv: An open problem. Neural networks : the official journal of the International Neural Network Society, 132:297–308.
Jocher, G., Stoken, A., Borovec, J., NanoCode012, Chaurasia, A., TaoXie, Changyu, L., V, A., Laughing, tkianai, yxNONG, Hogan, A., lorenzomammana, AlexWang1900, Hajek, J., Diaconu, L., Marc, Kwon, Y., oleg, wanghaoyang0106, Defretin, Y., Lohia, A., ml5ah, Milanko, B., Fineran, B., Khromov, D., Yiwei, D., Doug, Durgesh, and Ingham, F. (2021). ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations.
Kanehisa, R. and Neto, A. (2019). Firearm detection using convolutional neural networks.
Kreiss, S., Bertoni, L., and Alahi, A. (2021). Openpifpaf: Composite fields for semantic keypoint detection and spatio-temporal association. CoRR, abs/2103.02440.
Lexipol (2021). Public safety policy manual 2020. https://sunnyvale.ca.gov/civicax/filebank/blobdload.aspx?BlobID=26744. [Online; Last accessed 10 Aug 2021].
Lim, J., Al Jobayer, M. I., Baskaran, V. M., Lim, J. M., See, J., and Wong, K. (2021). Deep multi-level feature pyramids: Application for non-canonical firearm detection in video surveillance. Engineering Applications of Artificial Intelligence, 97:104094.
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., and Dollár, P. (2015). Microsoft coco: Common objects in context.
Medjahed, S. A. (2015). A comparative study of feature extraction methods in images classification. International Journal of Image, Graphics and Signal Processing, 7:16– 23.
Muhammad, K., Ahmad, J., Lv, Z., Bellavista, P., Yang, P., and Baik, S. (2018). Efficient deep cnn-based fire detection and localization in video surveillance applications. IEEE Transactions on Systems, Man, and Cybernetics: Systems, PP.
Olmos, R., Tabik, S., and Herrera, F. (2017). Automatic handgun detection alarm in videos using deep learning.
Padilla, R., Netto, S., and da Silva, E. (2020). A survey on performance metrics for object-detection algorithms.
Pérez, F., Tabik, S., Castillo Lamas, A., Olmos, R., Fujita, H., and Herrera, F. (2020). Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance. Knowledge-Based Systems, 194:105590.
Raji, I. D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., and Denton, E. (2020). Saving face: Investigating the ethical concerns of facial recognition auditing. In Markham, A. N., Powles, J., Walsh, T., and Washington, A. L., editors, AIES ’20: AAAI/ACM Conference on AI, Ethics, and Society, New York, NY, USA, February 7-8, 2020, pages 145–151. ACM.
Rama Gaur, D. V. S. C. (2017). Classifiers in image processing. International Journal on Future Revolution in Computer Science and Communication Engineering, 3:22–24.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection.
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019). Generalized intersection over union: A metric and a loss for bounding box regression.
Rosebrock, A. (2019). Fire and smoke detection with Keras and Deep Learning. [link]. [Online; Last accessed 10 Jun 2021].
Rovatsos, M., Mittelstadt, B., and Koene, A. (2019). Landscape Summary: Bias in Algorithmic Decision-Making: What is bias in algorithmic decision-making, how can we identify it, and how can we mitigate it? UK Government.
Ruiz-Santaquiteria, J., Velasco-Mata, A., Vállez, N., Bueno, G., Alvarez-Garcia, J., and Deniz, O. (2020). Handgun detection using combined human pose and weapon appearance. Slobogin, C. (2003). Public privacy: Camera surveillance of public places andthe right to anonymity.
University, G. (2019). What is Public Safety and Where Do You Fit in? [link]. [Online; Last accessed 15 Jun 2021].
Wilson, B., Hoffman, J., and Morgenstern, J. (2019). Predictive inequity in object detection. CoRR, abs/1902.11097.
Zhang, A., Lipton, Z. C., Li, M., and Smola, A. J. (2021). Dive into deep learning. arXiv preprint arXiv:2106.11342.
Publicado
29/11/2021
Como Citar
MOURA, Natan Santos; GONDIM, João Medrado; CLARO, Daniela Barreiro; SOUZA, Marlo; FIGUEIREDO, Roberto de Cerqueira.
Detection of weapon possession and fire in Public Safety surveillance cameras. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 290-301.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2021.18261.
