Smart City Gnosys

Smart city article details

Title	Traffic Monitoring Using An Object Detection Framework With Limited Dataset
ID_Doc	58636
Authors	Komasilovs V.; Zacepins A.; Kviesis A.; Estevez C.
Year	2019
Published	VEHITS 2019 - Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems
DOI	http://dx.doi.org/10.5220/0007586802910296
Abstract	Vehicle detection and tracking is one of the key components of the smart traffic concept. Modern city planning and development is not achievable without proper knowledge of existing traffic flows within the city. Surveillance video is an undervalued source of traffic information, which can be discovered by variety of information technology tools and solutions, including machine learning techniques. A solution for real-time vehicle traffic monitoring, tracking and counting is proposed in Jelgava city, Latvia. It uses object detection model for locating vehicles on the image from outdoor surveillance camera. Detected vehicles are passed to tracking module, which is responsible for building vehicle trajectory and its counting. This research compares two different model training approaches (uniform and diverse data sets) used for vehicle detection in variety of weather and day-time conditions. The system demonstrates good accuracy of given test cases (about 92% accuracy in average). In addition, results are compared to non-machine learning vehicle tracking approach, where notable vehicle detection accuracy increase is demonstrated on congested traffic. This research is fulfilled within the RETRACT (Enabling resilient urban transportation systems in smart cities) project. © 2019 by SCITEPRESS - Science and Technology Publications, Lda.
Author Keywords	Object Detection; Smart City; Tensorflow; Traffic Monitoring; Video Processing

Similar Articles

Id	Similarity	Authors	Title	Published
7402	0.901	Ghosh A.; Sabuj M.S.; Sonet H.H.; Shatabda S.; Farid D.M.	An Adaptive Video-Based Vehicle Detection, Classification, Counting, And Speed-Measurement System For Real-Time Traffic Data Collection	Proceedings of 2019 IEEE Region 10 Symposium, TENSYMP 2019 (2019)
4996	0.892	Bidwe R.V.; Bidwe S.; Zope B.	A Study Of Traffic Monitoring Systems For Smart City	2023 International Conference on Integration of Computational Intelligent System, ICICIS 2023 (2023)
22093	0.89	Hua S.; Anastasiu D.C.	Effective Vehicle Tracking Algorithm For Smart Traffic Networks	Proceedings - 13th IEEE International Conference on Service-Oriented System Engineering, SOSE 2019, 10th International Workshop on Joint Cloud Computing, JCC 2019 and 2019 IEEE International Workshop on Cloud Computing in Robotic Systems, CCRS 2019 (2019)
44480	0.889	Srikanth M.; Krishna N.S.V.S.S.J.; Krishna S.J.S.; Irfan S.; Venkat T.G.	Real-Time Vehicle Detection And Road Condition Prediction For Smart Urban Areas	Proceedings of the 4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024 (2024)
5541	0.887	Lopez L.; Gacitua R.	A Technological Proposal For Vehicle Detection In The Context Of Smart Cities	Proceedings of the 2019 IEEE 1st Sustainable Cities Latin America Conference, SCLA 2019 (2019)
60898	0.886	Alsanabani A.A.; Ahmed M.A.; Al Smadi A.M.	Vehicle Counting Using Detecting-Tracking Combinations: A Comparative Analysis	ACM International Conference Proceeding Series, PartF168342 (2020)
58424	0.886	Ahmed S.H.; Raza M.; Kazmi M.; Mehdi S.S.; Rehman I.; Qazi S.A.	Towards The Next Generation Intelligent Transportation System: A Vehicle Detection And Counting Framework For Undisciplined Traffic Conditions	Neural Network World, 33, 3 (2023)
60916	0.885	Shihabudeen H.; Rajeesh J.	Vehicle Detection In Smart City Surveillance Using Emerging Technologies	ICISTSD 2022 - 3rd International Conference on Innovations in Science and Technology for Sustainable Development (2022)
50560	0.885	Baiat Z.E.; Baydere S.	Smart City Traffic Monitoring:Yolov7 Transfer Learning Approach For Real-Time Vehicle Detection	2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023 (2023)
36028	0.885	Roslan R.; Ng S.; Yee L.C.	Machine Learning Techniques For Sustainable Smart Cities Traffic Management	Journal of Advanced Research in Applied Sciences and Engineering Technology, 33, 1 (2023)