Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles

Authors

  • Sachin Upadhye Department of Computer Application, Shri Ramdeobaba College of Engineering and Management, Nagpur
  • S. Neelakandan Department of CSE, R.M.K Engineering College, Chennai, India https://orcid.org/0000-0001-8583-0019
  • K. Thangaraj Department of IT, Sona College of Technology, Salem, India https://orcid.org/0000-0002-4346-3983
  • D. Vijendra Babu School of Electronics Engineering, Vellore Institute of Technology, Vellore – 632 014, Tamil Nadu, India https://orcid.org/0000-0001-9925-979X
  • N. Arulkumar Department of Data Science and Statistics, CHRIST (Deemed to be University), Bangalore, India
  • Kashif Qureshi Department of CSE, Sanskriti University Mathura, Uttar Pradesh, India https://orcid.org/0000-0001-8836-3145

DOI:

https://doi.org/10.13052/jmm1550-4646.1926

Keywords:

Deep learning, traffic flow monitoring, unmanned aerial vehicles, video surveillance, vehicle counting, speed estimation

Abstract

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.

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Author Biographies

Sachin Upadhye, Department of Computer Application, Shri Ramdeobaba College of Engineering and Management, Nagpur

Sachin Upadhye is an Assistant Professor at Computer Application Department of Shri Ramdeobaba College of Engineering and Management, Nagpur. He holds MCA, M. Tech. & PhD degrees in Computer Science & Engineering. He is an Oracle Certified Associate, IBM Rational Certified developer and has more than 20 research paper published in National & International Journals and conferences. He has 12 Years teaching experiences. He has successfully completed many certifications from NPTEL, IIT and also completed the research proposal and project.

S. Neelakandan, Department of CSE, R.M.K Engineering College, Chennai, India

S. Neelakandan (Senior Member, IEEE) is working as an Assistant Professor in the Department of CSE at R.M.K Engineering College Chennai. He has 14 years of Teaching experience. He has obtained his Bachelor of Engineering in Computer Science & Engineering M.E in Computer Science and Engineering from Anna University Chennai. He has received Ph.D in Information and Communication Engineering from Anna University. His research interests includes Data Science, Machine Learning, Big Data and Cloud Computing. He has published more than 30 research papers. He is a recipient of several awards for his credits and Reviewer for several International Journals. He also a Senior IEEE member, Life member of ISTE&IAENG.

K. Thangaraj, Department of IT, Sona College of Technology, Salem, India

K. Thangaraj, currently working as a Sr. Grade Assistant Professor cum Researcher in the Information Technology Department of Sona College of Technology with more than 14 years of Experience in Teaching and Research field. His current research focus is in the Design and Development of Energy Efficient Secure Wireless Protocols, and other research interests include Cloud Computing, Machine Learning and Internet of Things.

D. Vijendra Babu, School of Electronics Engineering, Vellore Institute of Technology, Vellore – 632 014, Tamil Nadu, India

D. Vijendra Babu obtained his B.E. from University of Madras, India, M.Tech. from SASTRA, Tanjore, India & Ph.D. from Jawaharlal Nehru Technological University, Hyderabad, India. He is currently designated as Vice Principal & Professor in the Department of Electronics and Communication Engineering at Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (VMRF), Tamil Nadu, India. He has 22 Years of Experience in the field of Education, Research & Administration at various levels. He has obtained Grants for 3 Australian Patents, 1 Indian Design Patent & Published 5 Indian Patents. He has published 100+ Articles in Refereed International/National Journals & Conferences which are indexed in Scopus/SCIE/Publons and also Reviewer in various leading Journals/Conferences. He gas published 2 Book Chapters and authored 5 Books on Hands on Data Science, Python Crash Course, Computer Vision Programming, Machine Learning and its Applications & Essentials of Wireless Sensor Networks. He is a Member in Academic Council Mm& Board of Studies Member of VMRF. He has acted as Chair in 21 International/National Conferences & delivered 40 Invited Lectures. Apart from Academics, he is an active involvement in Professional Societies as a Life Member in IEEE, CSI, IETE, BES (I), ISTE, ACEEE and IACSIT. He is currently the Secretary, Robotics & Automation Society (RAS), IEEE Madras Section & Executive Committee Member, IETE Chennai Centre.

N. Arulkumar, Department of Data Science and Statistics, CHRIST (Deemed to be University), Bangalore, India

N. Arulkumar is currently working as an Assistant Professor at Christ (Deemed to be University), Bangalore, INDIA. He received a Ph.D. degree in Computer Science from Bharathidasan University, India in 2019. His research areas are Computer Networks, Cyber Security, and the Internet of Things (IoT). He published more than 30 research papers in both journals and conferences. He published 2 patents in the fields of communication and computer science. He has chaired many technical sessions and delivered more than 10 invited talks at the national and international levels. He has completed more than 33 certifications from IBM, Google, Amazon, etc. He passed the CCNA: Routing and Switching Exam in 2017. Additionally, he also passed the Networking Fundamentals in the year 2017 exam from Microsoft.

Kashif Qureshi, Department of CSE, Sanskriti University Mathura, Uttar Pradesh, India

Kashif Quarsh After having applied research and academic experience of 21+ years of various countries (US, U.K, Australia, Saudi and Libya) and cultures now it is the right time to indoctrinate my scientific and soft skills to the experience for the long term in an international reputed universities. I am much spellbound in teaching Big Data, Cloud Computing, IoT, Data Security & Data Science (Artificial Intelligence, Machine Learning & Deep Learning) as the ensuing epoch will be an intelligent machines era and non-explicit programming will be much preferable. I have published books on Artificial Intelligence, Machine Learning, Operating System and Computer fundamentals, I am sure my core experience is a quick strong learning tool for students. My Ph.D. Masters and Bachelor’s Degrees were highly dedicated towards my interest and numerous students already benefitted. I have acted on the various post and served Universities and students in all ranges across 6 Countries.

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Published

2022-11-15

Issue

Section

New Trends in Real-Time Image and Video Processing for Surveillance and Security