{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T18:46:58Z","timestamp":1792090018316,"version":"4.4.0"},"reference-count":51,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    In recent days, vehicular accident detection has materialized because of the unique and intriguing application potential in the evolution of Intelligent Transportation Systems (ITS). To monitor and respond to the event of a fatal accident, real-world traffic surveillance footage requires continuous analysis. In this research, the road accident from the traffic surveillance video is detected, classified, and vehicle tracked using an efficient and innovative model. The surveillance videos are gathered from the CCTV Footage dataset, which is a publicly available dataset. The gathered videos are directly applied to the Spatio-Temporal Attention-based Adaptive Yolov9 with SE-InceptionNet and Deep SORT (AYSIDSORT) model for detecting, classifying and tracking traffic accidents very effectively. The developed network allows accurate detection and tracking of vehicles in real-time, which ensures the continuous traffic flow without losing moving objects. The variables of the proposed AYSIDSORT model are tuned by the developed Generation Probability-based Equilibrium Optimizer (GP-EO) to improve the detection and classification performance. The exact location of the accident is sent to the appropriate entities using an Internet of Things (IoT) platform to provide a timely rescue for the affected individual. Here, the proposed framework acts as a computer vision-based accident detection and tracking framework with potential IoT integration to find the exact location of the accident, which helps to provide a timely rescue for the affected individual. The effectiveness of the offered approach is validated with various baseline detection models. The accuracy of the suggested AYSIDSORT reaches to 95% in the 100\n                    <jats:sup>th<\/jats:sup>\n                    epoch, whereas there is a slight variation in the accuracy values when the batch size is varied from 4 to 128. The outcomes show that the designed framework is better than existing methods.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0357111","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:49:11Z","timestamp":1792086551000},"page":"e0357111","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Traffic surveillance video-based a unified road accident detection, classification, and vehicle tracking framework using an optimized spatio-temporal attention mechanism"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7296-1883","authenticated-orcid":true,"given":"Chokkakula","family":"Devi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gowri","family":"S.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"issue":"2","key":"pone.0357111.ref001","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/6979.880968","article-title":"Traffic monitoring and accident detection at intersections","volume":"1","author":"S Kamijo","year":"2000","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"pone.0357111.ref002","doi-asserted-by":"crossref","first-page":"106019","DOI":"10.1016\/j.aap.2021.106019","article-title":"A real-time video surveillance system for traffic pre-events detection","volume":"154","author":"A Pramanik","year":"2021","journal-title":"Accid Anal Prev"},{"key":"pone.0357111.ref003","doi-asserted-by":"crossref","first-page":"116389","DOI":"10.1016\/j.eswa.2021.116389","article-title":"An efficient automatic accident detection system using inertial measurement through machine learning techniques for powered two wheelers","volume":"192","author":"J Mahariba","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"pone.0357111.ref004","doi-asserted-by":"crossref","first-page":"113245","DOI":"10.1016\/j.chaos.2023.113245","article-title":"Deep learning model of convolutional neural networks powered by a genetic algorithm for prevention of traffic accidents severity","volume":"169","author":"L P\u00e9rez-Sala","year":"2023","journal-title":"Chaos, Solitons & Fractals"},{"key":"pone.0357111.ref005","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1016\/j.procs.2015.08.615","article-title":"Detection of road accident accumulation zones with a visual analytics approach","volume":"64","author":"L Ramos","year":"2015","journal-title":"Procedia Computer Sci"},{"key":"pone.0357111.ref006","doi-asserted-by":"crossref","first-page":"127453","DOI":"10.1109\/ACCESS.2019.2939532","article-title":"An automatic car accident detection method based on cooperative vehicle infrastructure systems","volume":"7","author":"D Tian","year":"2019","journal-title":"IEEE Access"},{"key":"pone.0357111.ref007","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1016\/j.procs.2023.01.143","article-title":"Smart detection system for driver distraction: enhanced support vector machine classifier using analytical hierarchy process technique","volume":"218","author":"H Singh","year":"2023","journal-title":"Procedia Computer Sci"},{"key":"pone.0357111.ref008","doi-asserted-by":"crossref","first-page":"105405","DOI":"10.1016\/j.aap.2019.105405","article-title":"Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis","volume":"136","author":"AB Parsa","year":"2020","journal-title":"Accid Anal Prev"},{"key":"pone.0357111.ref009","doi-asserted-by":"crossref","first-page":"105392","DOI":"10.1016\/j.aap.2019.105392","article-title":"Highway crash detection and risk estimation using deep learning","volume":"135","author":"T Huang","year":"2020","journal-title":"Accid Anal Prev"},{"issue":"12","key":"pone.0357111.ref010","doi-asserted-by":"crossref","first-page":"4795","DOI":"10.1109\/JSEN.2017.2777786","article-title":"An IoT cloud system for traffic monitoring and vehicular accidents prevention based on mobile sensor data processing","volume":"18","author":"A Celesti","year":"2018","journal-title":"IEEE Sensors Journal"},{"issue":"9","key":"pone.0357111.ref011","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/TITS.2017.2769719","article-title":"Event detection on roads using perceptual video summarization","volume":"19","author":"SS Thomas","year":"2018","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"pone.0357111.ref012","doi-asserted-by":"crossref","first-page":"104838","DOI":"10.1016\/j.trc.2024.104838","article-title":"Roadside LiDAR placement for cooperative traffic detection by a novel chance constrained stochastic simulation optimization approach","volume":"167","author":"Y Chen","year":"2024","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"pone.0357111.ref013","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TITS.2022.3222769","article-title":"Detection of Road Accidents Using Synthetically Generated Multi-Perspective Accident Videos","author":"TK Vijay","year":"2022","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"pone.0357111.ref014","first-page":"255","article-title":"FPCC: Fast point cloud clustering-based instance segmentation for industrial bin-picking,","author":"Y Xu","year":"14","journal-title":"Neurocomputing"},{"issue":"11","key":"pone.0357111.ref015","doi-asserted-by":"crossref","first-page":"20601","DOI":"10.1109\/TITS.2022.3170648","article-title":"Object Interaction-Based Localization and Description of Road Accident Events Using Deep Learning","volume":"23","author":"KV Thakare","year":"2022","journal-title":"IEEE Trans Intell Transport Syst"},{"issue":"2","key":"pone.0357111.ref016","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TITS.2006.890070","article-title":"A traffic accident recording and reporting model at intersections","volume":"8","author":"Y-K Ki","year":"2007","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"12","key":"pone.0357111.ref017","doi-asserted-by":"crossref","first-page":"25335","DOI":"10.1109\/TITS.2022.3165156","article-title":"Hybrid RESNET and Regional Convolution Neural Network Framework for Accident Estimation in Smart Roads","volume":"23","author":"Y Djenouri","year":"2022","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"pone.0357111.ref018","article-title":"Deep loss convexification for learning iterative models","author":"Z Zhang","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"pone.0357111.ref019","doi-asserted-by":"crossref","first-page":"9601","DOI":"10.1109\/TITS.2022.3157254","article-title":"Traffic accident detection via self-supervised consistency learning in driving scenarios","volume":"23","author":"J Fang","year":"2022","journal-title":"IEEE Trans Intell Transport Syst"},{"issue":"2","key":"pone.0357111.ref020","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1109\/TITS.2020.3027494","article-title":"Near real-time freeway accident detection","volume":"23","author":"YW Liyanage","year":"2022","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"pone.0357111.ref021","first-page":"1","author":"H Ghahremannezhad","year":"2022"},{"issue":"8","key":"pone.0357111.ref022","doi-asserted-by":"crossref","first-page":"4112","DOI":"10.3390\/s23084112","article-title":"A Shallow Autoencoder Framework for Epileptic Seizure Detection in EEG Signals","volume":"23","author":"GH Khan","year":"2023","journal-title":"Sensors (Basel)"},{"issue":"4","key":"pone.0357111.ref023","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.3390\/s25041029","article-title":"Enhancing Clustering Efficiency in Heterogeneous Wireless Sensor Network Protocols Using the K-Nearest Neighbours Algorithm","volume":"25","author":"A Juwaied","year":"2025","journal-title":"Sensors (Basel)"},{"issue":"3","key":"pone.0357111.ref024","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.icte.2021.11.004","article-title":"Deep learning based detection and localization of road accidents from traffic surveillance videos","volume":"8","author":"K Pawar","year":"2022","journal-title":"ICT Express"},{"issue":"2","key":"pone.0357111.ref025","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/ICJECE.2022.3154294","article-title":"A Vision-Based System Design and Implementation for Accident Detection and Analysis via Traffic Surveillance Video","volume":"45","author":"Y Sui","year":"2022","journal-title":"IEEE Can J Electr Comput Eng"},{"issue":"1","key":"pone.0357111.ref026","doi-asserted-by":"crossref","first-page":"100153","DOI":"10.1016\/j.kjs.2023.10.017","article-title":"Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques","volume":"51","author":"MQ Kheder","year":"2024","journal-title":"Kuwait Journal of Science"},{"issue":"2","key":"pone.0357111.ref027","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/JIOT.2020.3008896","article-title":"An IoT-Based Vehicle Accident Detection and Classification System Using Sensor Fusion","volume":"8","author":"N Kumar","year":"2021","journal-title":"IEEE Internet Things J"},{"issue":"11","key":"pone.0357111.ref028","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2022.e11397","article-title":"A deep neural framework for real-time vehicular accident detection based on motion temporal templates","volume":"8","author":"S Bakheet","year":"2022","journal-title":"Heliyon"},{"key":"pone.0357111.ref029","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.trpro.2022.09.012","article-title":"Deep learning applied to road accident detection with transfer learning and synthetic images","volume":"64","author":"T Tamagusko","year":"2022","journal-title":"Transportation Research Procedia"},{"key":"pone.0357111.ref030","article-title":"A real time accident detection framework for traffic video analysis","volume":"16","author":"H Ghahremannezhad","year":"2020","journal-title":"Machine Learning and Data Mining"},{"issue":"1","key":"pone.0357111.ref031","first-page":"210","article-title":"An Accident Detection and Classification System Using Internet of Things and Machine Learning towards Smart City","volume":"14","author":"M Balfaqih","year":"2022","journal-title":"Sustainability, Smart Cities and Smart Villages and the Global Digital Transformation: Strategy and Community Engagement"},{"key":"pone.0357111.ref032","author":"Z Sedaghatjoo","year":"2024"},{"issue":"3","key":"pone.0357111.ref033","article-title":"Traffic fatalities prediction based on support vector machine","volume":"39","author":"T Li","year":"2016","journal-title":"Archives of Transport"},{"key":"pone.0357111.ref034","first-page":"71","author":"AK Agrawal","year":"2020"},{"key":"pone.0357111.ref035","article-title":"Improved feature extraction method based on Histogram of Oriented Gradients for pedestrian detection","author":"H Ameur","year":"2014","journal-title":"IEEE"},{"issue":"4","key":"pone.0357111.ref036","first-page":"27","article-title":"Forecasting road accidents using deep learning approach: policies to improve road safety","volume":"8","author":"S Angadi","year":"2024","journal-title":"Journal of Soft Computing in Civil Engineering"},{"issue":"01","key":"pone.0357111.ref037","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1609\/aaai.v34i01.5371","article-title":"Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection","volume":"34","author":"D Cheng","year":"2020","journal-title":"AAAI"},{"key":"pone.0357111.ref038","first-page":"3","article-title":"About improving the performance of the YOLOv9 person detection model","author":"LM Bozhukha","year":"2024","journal-title":"\u041f\u0438\u0442\u0430\u043d\u043d\u044f \u043f\u0438\u043a\u043b\u0430\u0434\u043d\u043e\u0457 \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u043a\u0438 \u0456 \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u043d\u043e\u0433\u043e \u043c\u043e\u0434\u0435\u043b\u044e\u0432\u0430\u043d\u043d\u044f"},{"key":"pone.0357111.ref039","doi-asserted-by":"crossref","first-page":"995","DOI":"10.3390\/diagnostics12040995","article-title":"A multi-branch convolutional neural network with squeeze-and-excitation attention blocks for EEG-based motor imagery signals classification","volume":"12","author":"GA Altuwaijri","year":"2022","journal-title":"Diagnostics"},{"issue":"01","key":"pone.0357111.ref040","article-title":"Exploring pretrained deep learning models for the classification of electromagnetic radiation images","volume":"05","author":"G Parthiban","year":"2023","journal-title":"ICTACT Journal on Data Science and Machine Learning"},{"issue":"3","key":"pone.0357111.ref041","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.3390\/app12031319","article-title":"Sort and Deep-SORT Based Multi-Object Tracking for Mobile Robotics: Evaluation with New Data Association Metrics","volume":"12","author":"R Pereira","year":"2022","journal-title":"Applied Sciences"},{"key":"pone.0357111.ref042","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.105190","article-title":"Equilibrium optimizer: A novel optimization algorithm","volume":"191","author":"A Faramarzi","year":"2020","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"pone.0357111.ref043","first-page":"45","article-title":"Orangutan optimization algorithm: An innovative bio-inspired metaheuristic approach for solving engineering optimization problems","volume":"18","author":"T Hamadneh","year":"2025","journal-title":"Int J Intell Eng Syst"},{"key":"pone.0357111.ref044","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.knosys.2014.05.004","article-title":"Crisscross optimization algorithm and its application","volume":"67","author":"o Meng A n- b","year":"2014","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"pone.0357111.ref045","doi-asserted-by":"crossref","first-page":"6273","DOI":"10.1038\/s41598-025-89840-1","article-title":"Flower fertilization optimization algorithm with application to adaptive controllers","volume":"15","author":"H Albedran","year":"2025","journal-title":"Sci Rep"},{"key":"pone.0357111.ref046","article-title":"Livestream sales prediction based on an interpretable deep-learning model","author":"L Wang","year":"2024","journal-title":"Scientific Reports"},{"issue":"1","key":"pone.0357111.ref047","doi-asserted-by":"crossref","DOI":"10.1007\/s40747-024-01693-9","article-title":"Transforming traffic accident investigations: a virtual-real-fusion framework for intelligent 3D traffic accident reconstruction","volume":"11","author":"Y Chen","year":"2024","journal-title":"Complex Intell Syst"},{"key":"pone.0357111.ref048","article-title":"An adaptive fused domain-cycling variational generative adversarial network for machine fault diagnosis under data scarcity","volume":"126","author":"in Wang X","year":"2026","journal-title":"Information Fusion"},{"key":"pone.0357111.ref049","doi-asserted-by":"crossref","unstructured":"Dhake D, Prakash G, Ingle R, Nain A, Kadam K, Gite S, et al. Enhanced traffic safety by road anomaly detection using YOLOv8 and machine learning models. Advancements in IoT sensors and security. 2026. p. 443\u201367.","DOI":"10.1007\/978-3-032-05507-1_19"},{"issue":"2","key":"pone.0357111.ref050","doi-asserted-by":"crossref","first-page":"564","DOI":"10.3390\/s26020564","article-title":"YOLO-ERCD: An Upgraded YOLO Framework for Efficient Road Crack Detection","volume":"26","author":"X Li","year":"2026","journal-title":"Sensors"},{"issue":"8","key":"pone.0357111.ref051","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.3390\/s26082375","article-title":"LRD-DETR: A Lightweight RT-DETR-Based Model for Road Distress Detection","volume":"26","author":"C Dong","year":"2026","journal-title":"Sensors (Basel)"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0357111","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:49:20Z","timestamp":1792086560000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0357111"}},"subtitle":[],"editor":[{"given":"Nattapol","family":"Aunsri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,10,15]]},"references-count":51,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,10,15]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0357111","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,15]]}}}