{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:20:06Z","timestamp":1778170806007,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T00:00:00Z","timestamp":1672185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"scientific research","award":["NU\/NRP\/SERC\/11\/13"],"award-info":[{"award-number":["NU\/NRP\/SERC\/11\/13"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to improve driving safety, raise driver awareness of their driving patterns, and minimize future road accidents. Many symptoms appear to show this condition in the driver, such as facial expressions or abnormal actions. The abnormal activity was among the most common causes of road accidents, accounting for nearly 20% of all accidents, according to international data on accident causes. To avoid serious consequences, abnormal driving behaviors must be identified and avoided. As it is difficult to monitor anyone continuously, automated detection of this condition is more effective and quicker. To increase drivers\u2019 recognition of their driving behaviors and prevent potential accidents, a precise monitoring approach that detects abnormal driving behaviors and identifies abnormal driving behaviors is required. The most common activities performed by the driver while driving is drinking, eating, smoking, and calling. These types of driver activities are considered in this work, along with normal driving. This study proposed deep learning-based detection models for recognizing abnormal driver actions. This system is trained and tested using a newly created dataset, including five classes. The main classes include Driver-smoking, Driver-eating, Driver-drinking, Driver-calling, and Driver-normal. For the analysis of results, pre-trained and fine-tuned CNN models are considered. The proposed CNN-based model and pre-trained models ResNet101, VGG-16, VGG-19, and Inception-v3 are used. The results are compared by using the performance measures. The results are obtained 89%, 93%, 93%, 94% for pre-trained models and 95% by using the proposed CNN-based model. Our analysis and results revealed that our proposed CNN base model performed well and could effectively classify the driver\u2019s abnormal behavior.<\/jats:p>","DOI":"10.3390\/s23010311","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:38:43Z","timestamp":1672205923000},"page":"311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Unusual Driver Behavior Detection in Videos Using Deep Learning Models"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5635-9838","authenticated-orcid":false,"given":"Hamad Ali","family":"Abosaq","sequence":"first","affiliation":[{"name":"Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1770-8905","authenticated-orcid":false,"given":"Muhammad","family":"Ramzan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, University of Sargodha, Sargodha 40100, Pakistan"},{"name":"Department of Computer Science, University of Management & Technology, Lahore 54770, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faisal","family":"Althobiani","sequence":"additional","affiliation":[{"name":"Nautical Science Department, Faculty of Maritime Studies, King Abdulaziz University, Jeddah 22254, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adnan","family":"Abid","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Management & Technology, Lahore 54770, Pakistan"},{"name":"Faculty of Computer Science and Information Technology, Virtual University of Pakistan, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6852-7031","authenticated-orcid":false,"given":"Khalid Mahmood","family":"Aamir","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, University of Sargodha, Sargodha 40100, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hesham","family":"Abdushkour","sequence":"additional","affiliation":[{"name":"Nautical Science Department, Faculty of Maritime Studies, King Abdulaziz University, Jeddah 22254, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4161-6875","authenticated-orcid":false,"given":"Muhammad","family":"Irfan","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4364-1831","authenticated-orcid":false,"given":"Mohammad E.","family":"Gommosani","sequence":"additional","affiliation":[{"name":"Nautical Science Department, Faculty of Maritime Studies, King Abdulaziz University, Jeddah 22254, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saleh Mohammed","family":"Ghonaim","sequence":"additional","affiliation":[{"name":"Nautical Science Department, Faculty of Maritime Studies, King Abdulaziz University, Jeddah 22254, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V. R.","family":"Shamji","sequence":"additional","affiliation":[{"name":"Department of Hydrographic Surveying, Faculty of Maritime Studies, King Abdulaziz University, P.O. Box 80401, Jeddah 21589, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7262-183X","authenticated-orcid":false,"given":"Saifur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.32604\/cmc.2022.017522","article-title":"Automatic Unusual Activities Recognition Using Deep Learning in Academia","volume":"70","author":"Ramzan","year":"2022","journal-title":"CMC"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"61904","DOI":"10.1109\/ACCESS.2019.2914373","article-title":"A Survey on State-of-the-Art Drowsiness Detection Techniques","volume":"7","author":"Ramzan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","unstructured":"World Health Organization (2022, May 15). Road Traffic Injuries. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/road-traffic-injuries."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.trc.2015.03.036","article-title":"An integrated solution for lane level irregular driving detection on highways","volume":"56","author":"Sun","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"64571","DOI":"10.1109\/ACCESS.2019.2917213","article-title":"Video-based abnormal driving behavior detection via deep learning fusions","volume":"7","author":"Huang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1080\/00140131003769092","article-title":"Phoning while driving II: A review of driving conditions influence","volume":"53","author":"Collet","year":"2010","journal-title":"Ergonomics"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6645","DOI":"10.1109\/TVT.2017.2660497","article-title":"Abnormal Driving Detection Based on Normalized Driving Behavior","volume":"66","author":"Hu","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1109\/TITS.2010.2092770","article-title":"Driver inattention monitoring system for intelligent vehicles: A review","volume":"12","author":"Dong","year":"2010","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6943","DOI":"10.1109\/TVT.2020.2993247","article-title":"Abnormal Driving Detection with Normalized Driving Behavior Data: A Deep Learning Approach","volume":"69","author":"Hu","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, Z., Yu, J., Zhu, Y., Chen, Y., and Li, M. (2015, January 22\u201325). D3: Abnormal driving behaviors detection and identification using smartphone sensors. Proceedings of the 12th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2015, Seattle, WA, USA.","DOI":"10.1109\/SAHCN.2015.7338354"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6244","DOI":"10.1002\/int.22842","article-title":"An abnormal driving behavior recognition algorithm based on the temporal convolutional network and soft thresholding","volume":"37","author":"Zhao","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.jpdc.2022.01.010","article-title":"Driverrep: Driver identification through driving behavior embeddings","volume":"162","author":"Azadani","year":"2022","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.comcom.2021.12.007","article-title":"A lightweight framework for abnormal driving behavior detection","volume":"184","author":"Hou","year":"2022","journal-title":"Comput. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zhang, Y., and He, K. (July, January 29). Providing context-awareness in the smart car environment. Proceedings of the 10th IEEE International Conference on Computer and Information Technology, CIT-2010, Bradford, UK.","DOI":"10.1109\/CIT.2010.47"},{"key":"ref_15","unstructured":"Rakotonirainy, A. (2005, January 5). Design of context-aware systems for vehicle using complex systems paradigms. Proceedings of the CONTEXT-05 Workshop on Safety and Context, Paris, France."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sandberg, D., and Wahde, M. (2008, January 1\u20138). Particle swarm optimisation of feedforward neural networks for the detection of drowsy driving. Proceedings of the International Joint Conference on Neural Networks, Hong Kong, China.","DOI":"10.1109\/IJCNN.2008.4633886"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tateno, S., Guan, X., Cao, R., and Qu, Z. (2018, January 11\u201314). Development of Drowsiness Detection System Based on Respiration Changes Using Heart Rate Monitoring. Proceedings of the 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE, Nara, Japan.","DOI":"10.23919\/SICE.2018.8492599"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1177\/1550147719864883","article-title":"Internet of medical things for smart D3S to enable road safety","volume":"15","author":"Ramzan","year":"2019","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.1109\/TCSVT.2017.2769096","article-title":"Driver Facial Landmark Detection in Real Driving Situations","volume":"28","author":"Jeong","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1109\/JSEN.2018.2863023","article-title":"Grip and Electrophysiological Sensor-Based Estimation of Muscle Fatigue while Holding Steering Wheel in Different Positions","volume":"19","author":"Balasubramanian","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Eren, H., Makinist, S., Akin, E., and Yilmaz, A. (2012, January 3\u20137). Estimating driving behavior by a smartphone. Proceedings of the 2012 IEEE Intelligent Vehicles Symposium, Madrid, Spain.","DOI":"10.1109\/IVS.2012.6232298"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e3178","DOI":"10.1002\/dac.3178","article-title":"A driving behavior detection system based on a smartphone\u2019s built-in sensor","volume":"30","author":"Li","year":"2017","journal-title":"Int. J. Commun. Syst."},{"key":"ref_23","unstructured":"Promwongsa, N., Chaisatsilp, P., Supakwong, S., Saiprasert, C., Pholprasit, T., and Prathombutr, P. (2014, January 28\u201330). Automatic accelerometer reorientation for driving event detection using smartphone. Proceedings of the 13th ITS Asia Pacific Forum, Auckland, New Zealand."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4264","DOI":"10.1109\/TVT.2013.2263400","article-title":"Context-aware driver behavior detection system in intelligent transportation systems","volume":"62","author":"Zedan","year":"2013","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sysoev, M., Kos, A., Guna, J., and Poga\u010dnik, M. (2017). Estimation of the Driving Style Based on the Users\u2019 Activity and Environment Influence. Sensors, 17.","DOI":"10.3390\/s17102404"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.inffus.2022.08.009","article-title":"Distracted driving detection based on the fusion of deep learning and causal reasoning","volume":"89","author":"Ping","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, S., Wang, X., Ji, H., Wang, L., and Hou, Z. (2022). A Novel Driver Abnormal Behavior Recognition and Analysis Strategy and Its Application in a Practical Vehicle. Symmetry, 14.","DOI":"10.3390\/sym14101956"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Abtahi, S., Omidyeganeh, M., Shirmohammadi, S., and Hariri, B. (2014, January 19). YawDD: A yawning detection dataset. Proceedings of the 5th ACM Multimedia Systems Conference, Singapore.","DOI":"10.1145\/2557642.2563678"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"159027","DOI":"10.1109\/ACCESS.2020.3019503","article-title":"Video key frame monitoring algorithm and virtual reality display based on motion vector","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, M., Shu, H., and Jiang, J. (2009, January 5\u20136). An algorithm of key-frame extraction based on adaptive threshold detection of multi-features. Proceedings of the 2009 International Conference on Test and Measurement, Hong Kong, China.","DOI":"10.1109\/ICTM.2009.5412976"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/311\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:53:43Z","timestamp":1760147623000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/311"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,28]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010311"],"URL":"https:\/\/doi.org\/10.3390\/s23010311","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,28]]}}}