Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles
- Title
- Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles
- Creator
- Upadhye S.; Neelakandan S.; Thangaraj K.; Vijendra Babu D.; Arulkumar N.; Qureshi K.
- Description
- Recently, intelligent video surveillance technologies using unmanned aerial vehicles (UAVs) have been considerably increased in the transportation sector. Real time collection of traffic videos by the use of UAVs finds useful to monitor the traffic flow and road conditions. Since traffic jams have become common in urban areas, it is needed to design artificial intelligence (AI) based recognition techniques to attain effective traffic flow monitoring. Besides, the traffic flow monitoring system can assist the traffic managers to start efficient dispersal actions. Therefore, this study designs a real time traffic flow monitoring system using deep learning (DL) and UAVs, called RTTFM-DL. The proposed RTTFM-DL technique aims to detect vehicles, count vehicles, estimate speed and determine traffic flow. In addition, an efficient vehicle detection model is proposed by the use of Faster Regional Convolutional Neural Network (Faster RCNN) with Residual Network (ResNet). Also, a detection line based vehicle counting approach is designed, which is based on overlap ratio. Finally, traffic flow monitoring takes place based on the estimated vehicle count and vehicle speed. In order to guarantee the effectual performance of the RTTFM-DL technique, a series of experimental analyses take place and the results are examined under varying aspects. The experimental outcomes highlighted the betterment of the RTTFM-DL technique over the recent techniques. The RTTFM-DL technique has gained improved outcomes with a higher accuracy of 0.975. 2022 River Publishers.
- Source
- Journal of Mobile Multimedia, Vol-19, No. 2, pp. 477-496.
- Date
- 2023-01-01
- Publisher
- River Publishers
- Subject
- Deep learning; speed estimation; traffic flow monitoring; unmanned aerial vehicles; vehicle counting; video surveillance
- Coverage
- Upadhye S., Department of Computer Application, Shri Ramdeobaba College of Engineering and Management, Nagpur, India; Neelakandan S., Department of CSE, R.M.K Engineering College, Chennai, India; Thangaraj K., Department of IT, Sona College of Technology, Salem, India; Vijendra Babu D., School of Electronics Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, 632 014, India; Arulkumar N., Department of Data Science and Statistics, CHRIST (Deemed to be University), Bangalore, India; Qureshi K., Department of CSE, Sanskriti University, Uttar Pradesh, Mathura, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 15504646
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Upadhye S.; Neelakandan S.; Thangaraj K.; Vijendra Babu D.; Arulkumar N.; Qureshi K., “Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/14716.