Document Open Access Logo

A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)

Authors Zeba Khanam, Vejey Pradeep Suresh Achari, Issam Boukhennoufa , Anish Jindal , Amit Kumar Singh

PDF
Thumbnail PDF

File

OASIcs.NG-RES.2024.2.pdf
  • Filesize: 3.53 MB
  • 10 pages

Document Identifiers

Author Details

Zeba Khanam
  • BT Security Research, Adastral Park, UK
Vejey Pradeep Suresh Achari
  • Keele University, Keele, UK
Issam Boukhennoufa
  • University of Essex, Colchester, UK
Anish Jindal
  • Durham University, Durham, UK
Amit Kumar Singh
  • University of Essex, Colchester, UK

Cite AsGet BibTex

Zeba Khanam, Vejey Pradeep Suresh Achari, Issam Boukhennoufa, Anish Jindal, and Amit Kumar Singh. A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper). In Fifth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2024). Open Access Series in Informatics (OASIcs), Volume 117, pp. 2:1-2:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/OASIcs.NG-RES.2024.2

Abstract

Traffic congestion is one of the growing urban problem with associated problems like fuel wastage, loss of lives, and slow productivity. The existing traffic system uses programming logic control (PLC) with round-robin scheduling algorithm. Recent works have proposed IoT-based frameworks that use traffic density of each lane to control traffic movement, but they suffer from low accuracy due to lack of emergency vehicle image datasets for training deep neural networks. In this paper, we propose a novel distributed IoT framework that is based on two observations. The first observation is major structural changes to road are rare. This observation is exploited by proposing a novel two stage vehicle detector that is able to achieve 77% vehicle detection accuracy on UA-DETRAC dataset. The second observation is emergency vehicle have distinct siren sound that is detected using a novel acoustic detection algorithm on an edge device. The proposed system is able to detect emergency vehicles with an average accuracy of 99.4%.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time system architecture
Keywords
  • Vehicle Detection
  • Deep Neural Network
  • Traffic Control
  • Edge Computing
  • Emergency Vehicle Detection
  • Sliding Window

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Vejey Pradeep Suresh Achari, Zeba Khanam, Amit Kumar Singh, Anish Jindal, Alok Prakash, and Neeraj Kumar. I 2 UTS: An IoT based intelligent urban traffic system. In 2021 IEEE 22nd International Conference on High Performance Switching and Routing (HPSR), pages 1-6. IEEE, 2021. Google Scholar
  2. Stephan Patrick Baller, Anshul Jindal, Mohak Chadha, and Michael Gerndt. DeepEdgeBench: Benchmarking deep neural networks on edge devices. In 2021 IEEE International Conference on Cloud Engineering (IC2E), pages 20-30. IEEE, 2021. Google Scholar
  3. Sayalee Deshmukh and SB Vanjale. Iot based traffic signal control for reducing time delay of an emergency vehicle using gps. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pages 1-3. IEEE, 2018. Google Scholar
  4. Michał Grega, Andrzej Matiolański, Piotr Guzik, and Mikołaj Leszczuk. Automated detection of firearms and knives in a CCTV image. Sensors, 16(1):47, 2016. Google Scholar
  5. Jose Carlos Villarreal Guerra, Zeba Khanam, Shoaib Ehsan, Rustam Stolkin, and Klaus McDonald-Maier. Weather classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of convolutional neural networks. In 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), pages 305-310. IEEE, 2018. Google Scholar
  6. Yeong-Lin Lai, Yung-Hua Chou, and Li-Chih Chang. An intelligent IoT emergency vehicle warning system using RFID and Wi-Fi technologies for emergency medical services. Technology and health care, 26(1):43-55, 2018. Google Scholar
  7. Vamsi Paruchuri, Sriram Chellappan, and Rathinasamy B Lenin. Arrival time based traffic signal optimization for intelligent transportation systems. In 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), pages 703-709. IEEE, 2013. Google Scholar
  8. Sonam Pathak and Manish Pandey. Smart cities: Review of characteristics, composition, challenges and technologies. In 2021 6th International Conference on Inventive Computation Technologies (ICICT), pages 871-876. IEEE, 2021. Google Scholar
  9. Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint, 2018. URL: https://arxiv.org/abs/1804.02767.
  10. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. Google Scholar
  11. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 39(6):1137-1149, 2016. Google Scholar
  12. David Dorantes Romero, Anton Satria Prabuwono, A Hasniaty, et al. A review of sensing techniques for real-time traffic surveillance. Journal of applied sciences, 11(1):192-198, 2011. Google Scholar
  13. Shuvendu Roy and Md Sakif Rahman. Emergency vehicle detection on heavy traffic road from CCTV footage using deep convolutional neural network. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pages 1-6. IEEE, 2019. Google Scholar
  14. Mohd Saifuzzaman, Nazmun Nessa Moon, and Fernaz Narin Nur. Iot based street lighting and traffic management system. In 2017 IEEE region 10 humanitarian technology conference (R10-HTC), pages 121-124. IEEE, 2017. Google Scholar
  15. Rajeshwari Sundar, Santhoshs Hebbar, and Varaprasad Golla. Implementing intelligent traffic control system for congestion control, ambulance clearance, and stolen vehicle detection. IEEE Sensors Journal, 15(2):1109-1113, 2014. Google Scholar
  16. Mehal Zaman Talukder, Sheikh Shadab Towqir, Arifur Rahman Remon, and Hasan U Zaman. An IoT based automated traffic control system with real-time update capability. In 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pages 1-6. IEEE, 2017. Google Scholar
  17. Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, pages 6105-6114. PMLR, 2019. Google Scholar
  18. Longyin Wen, Dawei Du, Zhaowei Cai, Zhen Lei, Ming-Ching Chang, Honggang Qi, Jongwoo Lim, Ming-Hsuan Yang, and Siwei Lyu. UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking. Computer Vision and Image Understanding, 193:102907, 2020. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact