{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T14:40:52Z","timestamp":1779892852856,"version":"3.53.1"},"reference-count":68,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,11,24]],"date-time":"2019-11-24T00:00:00Z","timestamp":1574553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["H2020 call SEC-12-FCT-2016-Subtopic3 under the project VICTORIA (No. 740754)"],"award-info":[{"award-number":["H2020 call SEC-12-FCT-2016-Subtopic3 under the project VICTORIA (No. 740754)"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This paper shows a Novel Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to Command and Control Citizen Security Centers. This system was applied to the detection and classification of criminal events in a real-time video surveillance subsystem in the Command and Control Citizen Security Center of the Colombian National Police. It was developed using a novel application of Deep Learning, specifically a Faster Region-Based Convolutional Network (R-CNN) for the detection of criminal activities treated as \u201cobjects\u201d to be detected in real-time video. In order to maximize the system efficiency and reduce the processing time of each video frame, the pretrained CNN (Convolutional Neural Network) model AlexNet was used and the fine training was carried out with a dataset built for this project, formed by objects commonly used in criminal activities such as short firearms and bladed weapons. In addition, the system was trained for street theft detection. The system can generate alarms when detecting street theft, short firearms and bladed weapons, improving situational awareness and facilitating strategic decision making in the Command and Control Citizen Security Center of the Colombian National Police.<\/jats:p>","DOI":"10.3390\/info10120365","type":"journal-article","created":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T03:10:00Z","timestamp":1574651400000},"page":"365","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9577-0638","authenticated-orcid":false,"given":"Julio","family":"Suarez-Paez","sequence":"first","affiliation":[{"name":"Distributed Real-time Systems Laboratory (SATRD), Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mayra","family":"Salcedo-Gonzalez","sequence":"additional","affiliation":[{"name":"Distributed Real-time Systems Laboratory (SATRD), Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfonso","family":"Climente","sequence":"additional","affiliation":[{"name":"Distributed Real-time Systems Laboratory (SATRD), Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel","family":"Esteve","sequence":"additional","affiliation":[{"name":"Distributed Real-time Systems Laboratory (SATRD), Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4174-3762","authenticated-orcid":false,"given":"Jon Ander","family":"G\u00f3mez","sequence":"additional","affiliation":[{"name":"Pattern Recognition and Human Language Technologies, Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos Enrique","family":"Palau","sequence":"additional","affiliation":[{"name":"Distributed Real-time Systems Laboratory (SATRD), Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Israel","family":"P\u00e9rez-Llopis","sequence":"additional","affiliation":[{"name":"Distributed Real-time Systems Laboratory (SATRD), Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,24]]},"reference":[{"key":"ref_1","unstructured":"World Bank United Nations (2019). Perspectives of Global Urbanization, Command and Control and Cyber Research Portal (CCRP)."},{"key":"ref_2","unstructured":"Alberts, D.S., and Hayes, R.E. (2006). Understanding Command and Control the Future of Command and Control, Command and Control and Cyber Research Portal (CCRP)."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Esteve, M., Perez-Llopis, I., Hernandez-Blanco, L.E., Palau, C.E., and Carvajal, F. (2007, January 2\u20135). SIMACOP: Small Units Management C4ISR System. Proceedings of the IEEE International Conference Multimedia and Expo, Beijing, China.","DOI":"10.1109\/ICME.2007.4284862"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"163","DOI":"10.2991\/ijcis.11.1.13","article-title":"A dynamic multi-attribute group emergency decision making method considering experts\u2019 hesitation","volume":"11","author":"Wang","year":"2017","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MAES.2013.6470440","article-title":"Friendly force tracking COTS solution","volume":"28","author":"Esteve","year":"2013","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Esteve, M., P\u00e9rez-Llopis, I., Hern\u00e1ndez-Blanco, L., Martinez-Nohales, J., and Palau, C.E. (2009, January 18\u201321). Video sensors integration in a C2I system. Proceedings of the IEEE Military Communications Conference MILCOM, Boston, MA, USA.","DOI":"10.1109\/MILCOM.2009.5380090"},{"key":"ref_7","first-page":"398","article-title":"Advances in background updating and shadow removing for motion detection algorithms","volume":"Volume 3691","author":"Spagnolo","year":"2005","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Proceedings of the 11th International Conference on Computer Analysis of Images and Patterns, CAIP 2005, Versailles, France, 5\u20138 September 2005"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nieto, M., Varona, L., Senderos, O., Leskovsky, P., and Garcia, J. (2017, January 23\u201325). Real-time video analytics for petty crime detection. Proceedings of the 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016), Madrid, Spain.","DOI":"10.1049\/ic.2016.0091"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2945","DOI":"10.1109\/TIFS.2017.2725820","article-title":"Crowd Violence Detection Using Global Motion-Compensated Lagrangian Features and Scale-Sensitive Video-Level Representation","volume":"12","author":"Senst","year":"2017","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Machaca Arceda, V., Gutierrez, J.C., and Fernandez Fabian, K. (2016, January 20\u201322). Real Time Violence Detection in Video. Proceedings of the International Conference on Pattern Recognition Systems (ICPRS-16), Talca, Chile.","DOI":"10.1049\/ic.2016.0030"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bilinski, P., and Bremond, F. (2016, January 23\u201326). Human violence recognition and detection in surveillance videos. Proceedings of the 13th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2016, Colorado Springs, CO, USA.","DOI":"10.1109\/AVSS.2016.7738019"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xue, F., Ji, H., Zhang, W., and Cao, Y. (2018, January 16\u201318). Action Recognition Based on Dense Trajectories and Human Detection. Proceedings of the IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), Shenyang, China.","DOI":"10.1109\/AUTEEE.2018.8720753"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1109\/TMM.2017.2666540","article-title":"Sequential Deep Trajectory Descriptor for Action Recognition with Three-Stream CNN","volume":"19","author":"Shi","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dasari, R., and Chen, C.W. (2018, January 10\u201312). MPEG CDVS Feature Trajectories for Action Recognition in Videos. Proceedings of the IEEE 1th International Conference on Multimedia Information Processing and Retrieval, Miami, FL, USA.","DOI":"10.1109\/MIPR.2018.00069"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.procs.2018.07.059","article-title":"Human Action Recognition using 3D Convolutional Neural Networks with 3D Motion Cuboids in Surveillance Videos","volume":"133","author":"Arunnehru","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_16","first-page":"1","article-title":"Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures","volume":"49","author":"Kamel","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ren, J., Reyes, N.H., Barczak, A.L.C., Scogings, C., and Liu, M. (2018, January 13\u201315). Towards 3D human action recognition using a distilled CNN model. Proceedings of the IEEE 3rd International Conference Signal and Image Processing (ICSIP), Shenzhen, China.","DOI":"10.1109\/SIPROCESS.2018.8600485"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2326","DOI":"10.1109\/TIP.2018.2791180","article-title":"Real-Time Action Recognition with Deeply Transferred Motion Vector CNNs","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-Based Convolutional Networks for Accurate Object Detection and Segmentation","volume":"38","author":"Girshick","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 3\u20137). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Washington, DC, USA.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"123","DOI":"10.2991\/ijcis.2018.25905186","article-title":"Reduced computational cost prototype for street theft detection based on depth decrement in Convolutional Neural Network. Application to Command and Control Information Systems (C2IS) in the National Police of Colombia","volume":"12","author":"Esteve","year":"2018","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hao, S., Wang, P., and Hu, Y. (2019). Haze image recognition based on brightness optimization feedback and color correction. Information, 10.","DOI":"10.3390\/info10020081"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jiang, H., and Learned-Miller, E. (2017, January 3). Face Detection with the Faster R-CNN. Proceedings of the 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017\u20141st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Washington, DC, USA.","DOI":"10.1109\/FG.2017.82"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Peng, M., Wang, C., Chen, T., and Liu, G. (2016). NIRFaceNet: A convolutional neural network for near-infrared face identification. Information, 7.","DOI":"10.3390\/info7040061"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, S., and Zhang, L. (2018, January 8\u20139). Using Popular Object Detection Methods for Real Time Forest Fire Detection. Proceedings of the 11th International Symposium on Computational Intelligence and Design, ISCID, Hangzhou, China.","DOI":"10.1109\/ISCID.2018.00070"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, J., Miao, X., Jiang, H., Chen, J., and Liu, X. (2017, January 20\u201322). Identification of autonomous landing sign for unmanned aerial vehicle based on faster regions with convolutional neural network. Proceedings of the Chinese Automation Congress, CAC, Jinan, China.","DOI":"10.1109\/CAC.2017.8243120"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xu, W., He, J., Zhang, H.L., Mao, B., and Cao, J. (2016, January 23\u201326). Real-time target detection and recognition with deep convolutional networks for intelligent visual surveillance. Proceedings of the 9th International Conference on Utility and Cloud Computing\u2014UCC \u201816, New York, NY, USA.","DOI":"10.1145\/2996890.3007881"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 18\u201319). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the ACM International Conference on Multimedia\u2014MM \u201814, New York, NY, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_31","unstructured":"Nvidia Corporation (2019, November 24). NVIDIA CUDA\u00ae Deep Neural Network library (cuDNN). Available online: https:\/\/developer.nvidia.com\/cuda-downloads."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Song, D., Qiao, Y., and Corbetta, A. (2017, January 18\u201320). Depth driven people counting using deep region proposal network. Proceedings of the IEEE International Conference on Information and Automation, ICIA 2017, Macau SAR, China.","DOI":"10.1109\/ICInfA.2017.8078944"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Saikia, S., Fidalgo, E., Alegre, E., and Fern\u00e1ndez-Robles, L. (2017). Object Detection for Crime Scene Evidence Analysis Using Deep Learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Proceedings of the International Conference on Mobile and Wireless Technology, ICMWT 2017, Kuala Lumpur, Malaysia, 26\u201329 June 2017, Springer.","DOI":"10.1007\/978-3-319-68548-9_2"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sutanto, R.E., Pribadi, L., and Lee, S. (2017, January 26\u201329). 3D integral imaging based augmented reality with deep learning implemented by faster R-CNN. Proceedings of the Lecture Notes in Electrical Engineering, Proceedings of the International Conference on Mobile and Wireless Technology, ICMWT 2017, Kuala Lumpur, Malaysia.","DOI":"10.1007\/978-981-10-5281-1_26"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wu, X., Lu, X., and Leung, H. (2018). A video based fire smoke detection using robust AdaBoost. Sensors, 18.","DOI":"10.3390\/s18113780"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Park, J.H., Lee, S., Yun, S., Kim, H., Kim, W.-T., Park, J.H., Lee, S., Yun, S., Kim, H., and Kim, W.-T. (2019). Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework. Sensors, 19.","DOI":"10.3390\/s19092025"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Retuerta, D., Bartolom\u00e9, \u00c1., Chamoso, P., and Corchado, J.M. (2019). Counter-Terrorism Video Analysis Using Hash-Based Algorithms. Algorithms, 12.","DOI":"10.3390\/a12050110"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, B., Zhao, B., Tang, L., Han, Y., and Wang, W. (2018). Deep spatial-temporal joint feature representation for video object detection. Sensors, 18.","DOI":"10.3390\/s18030774"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, Z., and He, H. (2018). Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks. Symmetry, 10.","DOI":"10.3390\/sym10090375"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, Z., Zhang, L., Yang, Y., Kang, Q., Sun, D., Zhang, H., Zhang, Z., Zhang, L., and Yang, Y. (2019). Object Tracking for a Smart City Using IoT and Edge Computing. Sensors, 19.","DOI":"10.3390\/s19091987"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mazzeo, P.L., Giove, L., Moramarco, G.M., Spagnolo, P., and Leo, M. (September, January 30). HSV and RGB color histograms comparing for objects tracking among non overlapping FOVs, using CBTF. Proceedings of the 8th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2011, Washington, DC, USA.","DOI":"10.1109\/AVSS.2011.6027383"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Leo, M., Mazzeo, P.L., Mosca, N., D\u2019Orazio, T., Spagnolo, P., and Distante, A. (2008, January 7\u20139). Real-time multiview analysis of soccer matches for understanding interactions between ball and players. Proceedings of the International Conference on Content-based Image and Video Retrieval, Niagara Falls, ON, Canada.","DOI":"10.1145\/1386352.1386419"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1109\/TII.2018.2791944","article-title":"Secure surveillance framework for IoT systems using probabilistic image encryption","volume":"14","author":"Muhammad","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Barth\u00e9lemy, J., Verstaevel, N., Forehead, H., and Perez, P. (2019). Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City. Sensors, 19.","DOI":"10.3390\/s19092048"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Aqib, M., Mehmood, R., Alzahrani, A., Katib, I., Albeshri, A., and Altowaijri, S.M. (2019). Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs. Sensors, 19.","DOI":"10.3390\/s19092206"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Xu, S., Zou, S., Han, Y., and Qu, Y. (2018). Study on the availability of 4T-APS as a video monitor and radiation detector in nuclear accidents. Sustainability, 10.","DOI":"10.3390\/su10072172"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.future.2017.09.082","article-title":"Efficient IoT-based sensor BIG Data collection\u2014Processing and analysis in smart buildings","volume":"82","author":"Plageras","year":"2018","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_48","first-page":"30","article-title":"A Novel Approach on Visual Question Answering by Parameter Prediction using Faster Region Based Convolutional Neural Network","volume":"5","author":"Jha","year":"2019","journal-title":"Int. J. Interact. Multimed. Artif. Intell."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"29431","DOI":"10.1007\/s11042-018-6769-8","article-title":"A modified faster region-based convolutional neural network approach for improved vehicle detection performance","volume":"78","author":"Zhang","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cho, S., Baek, N., Kim, M., Koo, J., Kim, J., and Park, K. (2018). Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network. Sensors, 18.","DOI":"10.3390\/s18092995"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xing, W., Xing, M., and Sun, G. (2018). Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network. Sensors, 18.","DOI":"10.3390\/s18072327"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Liu, X., Jiang, H., Chen, J., Chen, J., Zhuang, S., and Miao, X. (2018, January 12\u201315). Insulator Detection in Aerial Images Based on Faster Regions with Convolutional Neural Network. Proceedings of the IEEE International Conference on Control and Automation, ICCA, Anchorage, AK, USA.","DOI":"10.1109\/ICCA.2018.8444172"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bakheet, S., and Al-Hamadi, A. (2016). A discriminative framework for action recognition using f-HOL features. Information, 7.","DOI":"10.3390\/info7040068"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Al-Gawwam, S., and Benaissa, M. (2018). Robust eye blink detection based on eye landmarks and Savitzky-Golay filtering. Information, 9.","DOI":"10.3390\/info9040093"},{"key":"ref_55","unstructured":"Krizhevsky, A., Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst."},{"key":"ref_56","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_58","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (\u20131, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Hou, R., Chen, C., and Shah, M. (2017, January 22\u201329). Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.620"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kalogeiton, V., Weinzaepfel, P., Ferrari, V., and Schmid, C. (2017, January 22\u201329). Action Tubelet Detector for Spatio-Temporal Action Localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.472"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zolfaghari, M., Oliveira, G.L., Sedaghat, N., and Brox, T. (2017, January 22\u201329). Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.316"},{"key":"ref_62","unstructured":"Nvidia Corporation (2019, November 24). Jetson Embedded Development Kit|NVIDIA. Available online: https:\/\/developer.nvidia.com\/embedded-computing."},{"key":"ref_63","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv."},{"key":"ref_64","unstructured":"Nvidia Corporation (2019, November 24). NVIDIA TensorRT|NVIDIA Developer. Available online: https:\/\/developer.nvidia.com\/tensorrt."},{"key":"ref_65","unstructured":"Nvidia Corporation (2019, November 24). NVIDIA DeepStream SDK|NVIDIA Developer. Available online: https:\/\/developer.nvidia.com\/deepstream-sdk."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Fraga-Lamas, P., Fern\u00e1ndez-Caram\u00e9s, T.M., Su\u00e1rez-Albela, M., Castedo, L., and Gonz\u00e1lez-L\u00f3pez, M. (2016). A Review on Internet of Things for Defense and Public Safety. Sensors, 16.","DOI":"10.3390\/s16101644"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Gomez, C.A., Shami, A., and Wang, X. (2018). Machine learning aided scheme for load balancing in dense IoT networks. Sensors, 18.","DOI":"10.3390\/s18113779"},{"key":"ref_68","unstructured":"(2019, November 24). AMD Embedded RadeonTM. Available online: https:\/\/www.amd.com\/en\/products\/embedded-graphics."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/12\/365\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:37:05Z","timestamp":1760189825000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/12\/365"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,24]]},"references-count":68,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["info10120365"],"URL":"https:\/\/doi.org\/10.3390\/info10120365","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,24]]}}}