{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:32:51Z","timestamp":1778693571630,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science, Technology, and Innovation Fund (FCTeI) of the General Royalties System (SGR)","award":["BPIN 2020000100044"],"award-info":[{"award-number":["BPIN 2020000100044"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This study addresses the challenge of detecting crimes against individuals in public security applications, particularly where the availability of quality data is limited, and existing models exhibit a lack of generalization to real-world scenarios. To mitigate the challenges associated with collecting extensive and labeled datasets, this study proposes the development of a novel dataset focused specifically on crimes against individuals, including incidents such as robberies, assaults, and physical altercations. The dataset is constructed using data from publicly available sources and undergoes a rigorous labeling process to ensure both quality and representativeness of criminal activities. Furthermore, a 3D convolutional neural network (Conv 3D) is implemented for real-time video analysis to detect these crimes effectively. The proposed approach includes a comprehensive validation of both the dataset and the model through performance comparisons with existing datasets, utilizing key evaluation metrics such as the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC). Experimental results demonstrate that the proposed dataset and model achieve an accuracy rate between 94% and 95%, highlighting their effectiveness in accurately identifying criminal activities. This study contributes to the advancement of crime detection technologies, offering a practical solution for implementation in surveillance and public safety systems in urban environments.<\/jats:p>","DOI":"10.3390\/a18020103","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T03:20:52Z","timestamp":1739503252000},"page":"103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Building a Custom Crime Detection Dataset and Implementing a 3D Convolutional Neural Network for Video Analysis"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3454-4383","authenticated-orcid":false,"given":"Juan Camilo","family":"Londo\u00f1o Lopera","sequence":"first","affiliation":[{"name":"Department of Electrical Energy and Automation, Faculty of Mines, Universidad Nacional de Colombia, Ave Cra 30 #45-3, Bogot\u00e1 111321, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3123-5481","authenticated-orcid":false,"given":"Freddy","family":"Bola\u00f1os Martinez","sequence":"additional","affiliation":[{"name":"Department of Electrical Energy and Automation, Faculty of Mines, Universidad Nacional de Colombia, Ave Cra 30 #45-3, Bogot\u00e1 111321, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1294-137X","authenticated-orcid":false,"given":"Luis Alejandro","family":"Fletscher Bocanegra","sequence":"additional","affiliation":[{"name":"Department of Electronic and Telecommunications Engineering, Faculty of Engineering, Universidad de Antioquia, Cl. 67 #53-108, Medell\u00edn 050010, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Bol\u00edvar, M.P. (2015). Understanding the Smart City Domain: A Literature Review. Transforming City Governments for Successful Smart Cities, Springer International Publishing.","DOI":"10.1007\/978-3-319-03167-5"},{"key":"ref_2","unstructured":"UN-Habitat (2020). World Cities Report 2020: The Value of Sustainable Urbanization, United Nations Human Settlements Programme."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.comnet.2015.12.023","article-title":"Urban planning and building smart cities based on the Internet of Things using Big Data analytics","volume":"101","author":"Rathore","year":"2016","journal-title":"Comput. Netw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"337","DOI":"10.2298\/SJEE2003337S","article-title":"Big Data and Development of Smart City: System Architecture and Practical Public Safety Example","volume":"17","year":"2020","journal-title":"Serbian J. Electr. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s13174-015-0041-5","article-title":"Applications of Big Data to Smart Cities","volume":"6","author":"Mohamed","year":"2015","journal-title":"J. Internet Serv. Appl."},{"key":"ref_6","unstructured":"(2023, March 22). OECD Better Life Index. Available online: https:\/\/www.oecdbetterlifeindex.org\/."},{"key":"ref_7","unstructured":"Polic\u00eda Nacional de Colombia (2023, March 22). Estad\u00edstica Delictiva, Available online: https:\/\/www.policia.gov.co\/estadistica-delictiva."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e3843","DOI":"10.1002\/ett.3843","article-title":"LAMSTAR: For IoT-based face recognition system to manage the safety factor in smart cities","volume":"31","author":"Medapati","year":"2019","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_9","first-page":"698","article-title":"Face Detection and Recognition Based on Deep Learning in the Monitoring Environment","volume":"901","author":"Zhu","year":"2018","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.future.2017.08.006","article-title":"Real-time secure communication for Smart City in high-speed Big Data environment","volume":"83","author":"Rathore","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1049\/ipr2.12258","article-title":"Anomaly Detection in Video Sequences: A Benchmark and Computational Model","volume":"15","author":"Wan","year":"2021","journal-title":"IET Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ullah, W., Ullah, A., Hussain, T., Khan, A., and Baik, S.W. (2021). An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos. Sensors, 21.","DOI":"10.3390\/s21082811"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"16979","DOI":"10.1007\/s11042-020-09406-3","article-title":"CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks","volume":"80","author":"Ullah","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7071","DOI":"10.1109\/TVT.2019.2918576","article-title":"SPATH: Finding the Safest Walking Path in Smart Cities","volume":"68","author":"Pang","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.pmcj.2019.01.003","article-title":"Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments","volume":"53","author":"Catlett","year":"2019","journal-title":"Pervasive Mob. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1016\/S0169-2070(03)00092-X","article-title":"Short-term forecasting of crime","volume":"19","author":"Gorr","year":"2003","journal-title":"Int. J. Forecast."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1016\/j.future.2020.03.002","article-title":"Series mining for public safety advancement in emerging smart cities","volume":"108","author":"Isafiade","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C., and Shah, M. (2018, January 18\u201322). Real-world Anomaly Detection in Surveillance Videos. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00678"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhong, J.X., Li, N., Kong, W., Liu, S., Li, T.H., and Li, G. (2019, January 15\u201320). Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00133"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"76111","DOI":"10.1109\/ACCESS.2018.2883560","article-title":"Person Re-Identification by Multi-Camera Networks for Internet of Things in Smart Cities","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lv, H., Chen, C., Cui, Z., Xu, C., Li, Y., and Yang, J. (2021, January 19\u201325). Learning Normal Dynamics in Videos with Meta Prototype Network. Proceedings of the Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01517"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lv, H., Zhou, C., Cui, Z., Xu, C., Li, Y., and Yang, J. (2021, January 19\u201325). Localizing Anomalies from Weakly-Labeled Videos. Proceedings of the Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/TIP.2021.3072863"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, P., Liu, J., Shi, Y., Sun, Y., Shao, F., Wu, Z., and Yang, Z. (2020, January 23\u201328). Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision. Proceedings of the Computer Vision and Pattern Recognition, Glasgow, UK.","DOI":"10.1007\/978-3-030-58577-8_20"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107560","DOI":"10.1109\/ACCESS.2019.2932114","article-title":"A Review on State-of-the-Art Violence Detection Techniques","volume":"7","author":"Ramzan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1109\/TPAMI.2022.3193611","article-title":"Deep Learning-Based Action Detection in Untrimmed Videos: A Survey","volume":"45","author":"Vahdani","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014, January 6\u201312). Spatio-temporal Object Detection Proposals. Proceedings of the Computer Vision\u2013ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10599-4"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hou, R., Chen, C., and Shah, M. (2017). Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos. arXiv.","DOI":"10.1109\/ICCV.2017.620"},{"key":"ref_28","unstructured":"Cuzzolin, F., Singh, G., Saha, S., Sapienza, M., and Torr, P. (2017). Online Real time Multiple Spatiotemporal Action Localisation and Prediction on a Single Platform. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Weinzaepfel, P., Harchaoui, Z., and Schmid, C. (2015). Learning to track for spatio-temporal action localization. arXiv.","DOI":"10.1109\/ICCV.2015.362"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bojanowski, P., Lajugie, R., Bach, F.R., Laptev, I., Ponce, J., Schmid, C., and Sivic, J. (2014). Weakly Supervised Action Labeling in Videos Under Ordering Constraints. arXiv.","DOI":"10.1007\/978-3-319-10602-1_41"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Huang, D., Fei-Fei, L., and Niebles, J.C. (2016). Connectionist Temporal Modeling for Weakly Supervised Action Labeling. arXiv.","DOI":"10.1007\/978-3-319-46493-0_9"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Islam, A., and Radke, R.J. (2020). Weakly Supervised Temporal Action Localization Using Deep Metric Learning. arXiv.","DOI":"10.1109\/WACV45572.2020.9093620"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gao, M., Zhou, Y., Xu, R., Socher, R., and Xiong, C. (2020). WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos. arXiv.","DOI":"10.1109\/CVPR46437.2021.00195"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sener, F., and Yao, A. (2018). Unsupervised Learning and Segmentation of Complex Activities from Video. arXiv.","DOI":"10.1109\/CVPR.2018.00873"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kukleva, A., Kuehne, H., Sener, F., and Gall, J. (2019). Unsupervised learning of action classes with continuous temporal embedding. arXiv.","DOI":"10.1109\/CVPR.2019.01234"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ji, J., Cao, K., and Niebles, J.C. (2019). Learning Temporal Action Proposals With Fewer Labels. arXiv.","DOI":"10.1109\/ICCV.2019.00717"},{"key":"ref_37","unstructured":"Tarvainen, A., and Valpola, H. (2017). Weight-averaged consistency targets improve semi-supervised deep learning results. arXiv."},{"key":"ref_38","unstructured":"Lin, X., Shou, Z., and Chang, S. (2019). LPAT: Learning to Predict Adaptive Threshold for Weakly-supervised Temporal Action Localization. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jain, M., Ghodrati, A., and Snoek, C.G.M. (2020, January 13\u201319). ActionBytes: Learning From Trimmed Videos to Localize Actions. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00125"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, M., Li, B., Bao, Y., AlRegib, G., and Kira, Z. (2020). Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation. arXiv.","DOI":"10.1109\/CVPR42600.2020.00947"},{"key":"ref_41","unstructured":"Zhou, Z.H. (2021, January 19\u201326). Self-Supervised Video Action Localization with Adversarial Temporal Transforms. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, Virtual."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Acsintoae, A., Florescu, A., Georgescu, M.I., Mare, T., Sumedrea, P., Ionescu, R.T., Khan, F.S., and Shah, M. (2022, January 18\u201324). UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection. Proceedings of the Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01951"},{"key":"ref_43","unstructured":"Landi, F., Snoek, C.G.M., and Cucchiara, R. (2019). Anomaly Locality in Video Surveillance. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Boekhoudt, K., Matei, A., Aghaei, M., and Talavera, E. (2021). HR-Crime: Human-Related Anomaly Detection in Surveillance Videos. arXiv.","DOI":"10.1007\/978-3-030-89131-2_15"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cheng, M., Cai, K., and Li, M. (2021, January 10\u201315). RWF-2000: An Open Large Scale Video Database for Violence Detection. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412502"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Soliman, M.M., Kamal, M.H., El-Massih Nashed, M.A., Mostafa, Y.M., Chawky, B.S., and Khattab, D. (2019, January 8\u20139). Violence Recognition from Videos using Deep Learning Techniques. Proceedings of the 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/ICICIS46948.2019.9014714"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.patrec.2021.01.031","article-title":"Iterative weak\/self-supervised classification framework for abnormal events detection","volume":"145","author":"Degardin","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Perez, M., Kot, A.C., and Rocha, A. (2019, January 12\u201317). Detection of Real-world Fights in Surveillance Videos. Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683676"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.compbiomed.2010.12.006","article-title":"A new dataset evaluation method based on category overlap","volume":"41","author":"Oh","year":"2011","journal-title":"Comput. Biol. Med."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Althnian, A., AlSaeed, D., Al-Baity, H., Samha, A., Dris, A.B., Alzakari, N., Abou Elwafa, A., and Kurdi, H. (2021). Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain. Appl. 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