La Inteligencia Artificial como apoyo a la gestión de la seguridad ciudadana: un estado del arte

Oscar M. Morales, Luis A. Fletscher Bocanegra, Juan F. Botero Vega

Resumen


Cada vez más, existe un mayor interés en los gobiernos de múltiples ciudades de garantizar la seguridad de sus ciudadanos, siendo la tecnología una de las herramientas clave para enfrentar los diferentes retos que en este campo se plantean. En este trabajo se presenta una aproximación al estado del arte de algoritmos estadísticos y técnicas de inteligencia computacional utilizados en el campo de la gestión de problemas de seguridad ciudadana. Para esto se escogieron dos de las aplicaciones más relevantes en el área, las cuales son: detección de disparos y detección de armas. Para cada una de estas aplicaciones se hizo un cuadro con los trabajos más relevantes junto con un posterior análisis, se formularon las conclusiones y se describió el trabajo futuro. Finalmente, se organizó una lista de recomendaciones con los algoritmos más utilizados y que mejores resultados han arrojado.


Texto completo:

PDF

Referencias


Afandi, W.E.I.B.W.N.; Isa, N.M. (2021). Object Detection: Harmful Weapons Detection using YOLOv4. In 2021 IEEE Symposium on Wireless Technology Applications (ISWTA) (pp. 63-70). doi:10.1109/ISWTA52208.2021.9587423.

Ahmed, T.; Uppal, M.; Muhammad, A. (2013). Improving efficiency and reliability of gunshot detection systems. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 513-517). doi:10.1109/ICASSP.2013.6637700.

Alaqil, R. M.; ... (2020). Automatic Gun Detection From Images Using Faster R-CNN. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH) (pp. 149-154). doi:10.1109/SMART-TECH49988.2020.00045.

Ashraf, A. H.; ... (2021) Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s. (https://www.researchgate.net/profile/Muhammad-Khan-730/publication/354871087_Weapons_Detection_for_Security_and_Video_Surveillance_Using_CNN_and_YOLO-V5s/links/6151fef3d2ebba7be7522142/Weapons-Detection-for-Security-and-Video-Surveillance-Using-CNNand-YOLO-V5s.pdf).

Bajzik, J.; Prinosil, J.; Koniar, D. (2020). Gunshot Detection Using Convolutional Neural Networks. In 2020 24th International Conference Electronics (pp. 1-5). doi:10.1109/IEEECONF49502.2020.9141621.

BEN BOTKIN, The Statesman Journal (2019). Oregon weighs multiple new restrictions on firearms. (https://kval.com/news/local/in-oregon-it-isnt-a-question-of-if-firearms-legislation-will-pass-itswhich-ones).

Caragliu, A.; Del Bo, C.; Nijkamp, P. (2011). Smart Cities in Europe. Journal of Urban Technology, 18(2), 65-82. doi:10.1080/10630732.2011.601117.

Company (2020). ShotSpotter. (https://www.shotspotter.com/company/).

Debnath, R.; Bhowmik, M. K. (2020). Automatic Visual Gun Detection Carried by A Moving Person. In 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) (pp. 208-213). doi:10.1109/ICIIS51140.2020.9342681.

Desk, T. (2018). CCTV VIDEO: Robber pulls out gun; man in cowboy hat tackles him to the ground. The Indian Express, 26 abril. (https://indianexpress.com/article/trending/viral-videos-trending/armed-robber-steps-shop-man-cowboy-hat-tackles-him-mexico-5152558/).

Dextre, M.; ... (2021). Gun Detection in Real-Time, using YOLOv5 on Jetson AGX Xavier. In 2021 XLVII Latin American Computing Conference (CLEI) (pp. 1-7). doi:10.1109/CLEI53233.2021.9640100.

Farrelly, C. M. (2018). Key algorithms and statistical models for aspiring data scientists, KDnuggets. (https://www.kdnuggets.com/2018/04/key-algorithms-statistical-models-aspiring-data-scientists.html).

Departamento Nacional de Planeación DNP (2020). Documento de Lineamientos de Política de Ciudades Inteligentes (Borrador). (https://www.dnp.gov.co/DNPN/Paginas/Lineamientos-de-politica-de-ciudades-inteligentes.aspx).

Fansler, K. S. (1998). Description of muzzle blast by modified ideal scaling models. Shock and Vibration Digest, 5(1), 1-12. doi:10.1155/1998/640253.

Freytag, J. C.; Begault, D. R.; Peltier, C. A. (2006). The acoustics of gunfire. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings. Institute of Noise Control Engineering (pp. 1165-1174).

Gaidon, A.; … (2016). Virtual worlds as proxy for multi-object tracking analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4340-4349). (https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Gaidon_Virtual_Worlds_as_CVPR_2016_paper.html).

Galangque, C. M. J.; Guirnaldo, S. A. (2019). Gunshot Classification and Localization System using Artificial Neural Network (ANN). In 2019 12th International Conference on Information Communication Technology and System (ICTS) (pp. 1-5). doi:10.1109/ICTS.2019.8850937.

Głomb, P.; … (2018). Application of hyperspectral imaging and machine learning methods for the detection of gunshot residue patterns. Forensic science international, 290, 227-237. doi:10.1016/j.forsciint.2018.06.040.

Hashmi, T. S. S.; ... (2021). Application of Deep Learning for Weapons Detection in Surveillance Videos. In 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) (pp. 1-6). doi:10.1109/ICoDT252288.2021.9441523.

Hrabina, M.; Sigmund, M. (2016). Implementation of developed gunshot detection algorithm on TMS320C6713 processor. In 2016 SAI Computing Conference (SAI) (pp. 902-905). doi:10.1109/SAI.2016.7556087.

Hrabina, M.; Sigmund, M. (2018). Gunshot recognition using low level features in the time domain. In 2018 28th International Conference Radioelektronika (RADIOELEKTRONIKA) (pp. 1-5). doi:10.1109/RADIOELEK.2018.8376372.

Jain, A.; Aishwarya; Garg, G. (2020). Gun Detection with Model and Type Recognition using Haar Cascade Classifier. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 419-423). doi:10.1109/ICSSIT48917.2020.9214211.

Jain, H.; ... (2020). Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 193-198). doi:10.1109/ICESC48915.2020.9155832.

Jiang, Z. (2003). Wave dynamic processes induced by a supersonic projectile discharging from a shock tube. Physics of fluids, 15(6), 1665-1675. doi:10.1063/1.1566752.

Kacprzyk, J.; Pedrycz, W. (eds.) (2015). Springer Handbook of Computational Intelligence. Springer, Berlin, Heidelberg. doi:10.1007/978-3-662-43505-2.

Kiktova, E.; ... (2015). Gun type recognition from gunshot audio recordings. In 3rd International Workshop on Biometrics and Forensics (IWBF 2015) (pp. 1-6). doi:10.1109/IWBF.2015.7110240.

Lim, J.; ... (2019). Gun Detection in Surveillance Videos using Deep Neural Networks. In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 1998-2002).

Lindström, A.-C.; ... (2014). Detection of gunshot residues (GSR) on a self-inflicted gunshot wound. Pathology, 46(3), 260-263. doi:10.1097/PAT.0000000000000083.

McCarthy, J. (2007). What is artificial intelligence?. Stanford University.

McCoy, R. L. (s.f.). Modern exterior ballistics: the launch and flight dynamics of symmetric projectiles, 1999. Schiffer Publishing Ltd [Preprint]. Schiffer Publishing Ltd.

Maher, R. C. (2007). Acoustical Characterization of Gunshots. In 2007 IEEE Workshop on Signal Processing Applications for Public Security and Forensics (pp. 1-5). doi:10.1109/IEEECONF12259.2007.4218954.

Manjarres, W.; Baca, W. (2019). Victimización por crimen, percepción de seguridad y satisfacción con la vida en Colombia. Revista de Economía Institucional, 21(41), 133-160. doi:10.18601/01245996.v21n41.06.

Ministerio de Justicia de Colombia (2000). Código Penal Colombiano.

Mitchell, S.; Villa, N.; Stewart-Weeks, M.; Lange, A. (2013). The Internet of Everything for Cities. Connecting People, Proccess, Data, and Things to Improve the ‘Livability’ of Cities and Communities. San Jose Cisco. 2013.

Morehead, A.; ... (2019). Low Cost Gunshot Detection using Deep Learning on the Raspberry Pi. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 3038-3044). doi:10.1109/BigData47090.2019.9006456.

Narejo, S.; ... (2021). Weapon Detection Using YOLO V3 for Smart Surveillance System. Mathematical Problems in Engineering, 2021. doi:10.1155/2021/9975700.

Olmos, R.; Tabik, S.; Herrera, F. (2018). Automatic handgun detection alarm in videos using deep learning. Neurocomputing, 275, 66-72. doi:10.1016/j.neucom.2017.05.012.

Pikrakis, A.; Giannakopoulos, T.; Theodoridis, S. (2008). Gunshot detection in audio streams from movies by means of dynamic programming and Bayesian networks. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 21-24). doi:10.1109/ICASSP.2008.4517536.

Poole, D.; Mackworth, A. (2017). Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press.

Ren, S.; ... (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28. (https://proceedings.neurips.cc/paper/2015/hash/14bfa6bb14875e45bba028a21ed38046-Abstract.html).

Salazar González, J. L.; ... (2020). Real-time gun detection in CCTV: An open problem. Neural networks: the official journal of the International Neural Network Society, 132, 297-308. doi:10.1016/j.neunet.2020.09.013.

Samireddy, S. R.; Carletta, J.; Lee, K.-S. (2017). An embeddable algorithm for gunshot detection. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 68-71). doi:10.1109/MWSCAS.2017.8052862.

Shiekh, A. A.; Tahir, M.; Uppal, M. (2017). Accurate gunshot detection in urban environments using blind deconvolution. In 2017 International Multi-topic Conference (INMIC) (pp. 1-4). doi:10.1109/INMIC.2017.8289452.

Smith, G. J. D. (2004). Behind the Screens: Examining Constructions of Deviance and Informal Practices among CCTV Control Room Operators in the UK. Schweizerische Monatsschrift fur Zahnheilkunde = Revue mensuelle suisse d’odonto-stomatologie / SSO, 2(2/3). doi:10.24908/ss.v2i2/3.3384.

Soria Romo, R. (2017). El impacto de la inseguridad pública en la competitividad empresarial. Análisis comparativo de las entidades federativas en México. Economía y Sociedad, XXI(36), 19-41. (https://www.redalyc.org/articulo.oa?id=51052064002).

Su, K.; Li, J.; Fu, H. (2011). Smart city and the applications. In 2011 International Conference on Electronics, Communications and Control (ICECC) (pp. 1028-1031). doi:10.1109/ICECC.2011.6066743.

Tabane, E.; Ngwira, S. M.; Zuva, T. (2016). Survey of smart city initiatives towards urbanization. In 2016 International Conference on Advances in Computing and Communication Engineering (ICACCE) (pp. 437-440). doi:10.1109/ICACCE.2016.8073788.

Verma, G. K.; Dhillon, A. (2017). A Handheld Gun Detection using Faster R-CNN Deep Learning. In Proceedings of the 7th International Conference on Computer and Communication Technology. New York, NY, USA: Association for Computing Machinery (ICCCT-2017) (pp. 84-88). doi:10.1145/3154979.3154988.

Wandelt, S.; Bux, M.; Leser, U. (2014). Trends in Genome Compression. Current bioinformatics, 9(3). doi:10.2174/1574893609666140516010143.

Warsi, A.; ... (2019). Gun Detection System Using Yolov3. In 2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) (pp. 1-4). doi:10.1109/ICSIMA47653.2019.9057329.

Zhao, F.; ... (2021). Smart city research: A holistic and state-of-the-art literature review. Cities, 119, 103406. doi:10.1016/j.cities.2021.103406.


Enlaces refback

  • No hay ningún enlace refback.


Revista de Pensamiento Estratégico y Seguridad CISDE

ISSN: 2529-8763

www.uajournals.com/cisdejournal

cisdejournal@uajournals.com