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Canny edge detection based real-time intelligent parking management system

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Real-time traffic monitoring and parking are very important aspects for a better social and economic system. Python-based Intelligent Parking Management System (IPMS) module using a USB camera and a canny edge detection method was developed. The current situation of real-time parking slot was simultaneously checked, both online and via a mobile application, with a message of Parking “Available” or “Not available” for 10 parking slots. In addition, at the time entering in parking module, gate open and at the time of exit parking module, the gate closes automatically using servomotor and sensors. Results are displayed in figures with the proposed method flow chart.
Rocznik
Tom
Strony
197--208
Opis fizyczny
Bibliogr. 16 poz.
Twórcy
  • Faculty of Electronics & Communication Engineering Department, Gujarat Technological University, Government Engineering College Bhavnagar-364002, Gujarat, India
  • Principal, Ahmedabad Institute of Technolog-380060, Gujarat Technological University, Gujarat, India
autor
  • Faculty of Electronics & Communication Engineering Department, Gujarat Technological University, Government Engineering College Bhavnagar-364002, Gujarat, India
Bibliografia
  • 1. Akande N.O., et al. 2018. “Improving the quality of service in public road transportation using real time travel information system”. World Review of Intermodal Transportation Research 7(1): 57-79. DOI: 10.1504/WRITR.2018.089529.
  • 2. Al-Turjman F., A. Malekloo. 2019. “Smart parking in IoT-enabled cities: A survey”. Sustainable Cities and Society 49. DOI: 10.1016/j.scs.2019.101608.
  • 3. Anandhalli M., V.P. Baligar. 2018. “A novel approach in real-time vehicle detection and tracking using Raspberry Pi”. Alexandria Engineering Journal 57(3): 1597-1607. DOI: 10.1016/j.aej.2017.06.008.
  • 4. Antoniou, C., et al. 2018. “A framework for risk reduction for indoor parking facilities under constraints using positioning technologies”. International Journal of Disaster Risk Reduction 31: 1166-1176. DOI: 10.1016/j.ijdrr.2017.09.032.
  • 5. Asghari P., A.M. Rahmani, H.H.S. Javadi. 2019. “Internet of Things applications: A systematic review”. Computer Networks 148: 241-261. DOI: 10.1016/j.comnet.2018.12.008.
  • 6. Canny J. 1986. “A Computational Approach to Edge Detection”. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6): 679-698. DOI: 10.1109/TPAMI.1986.4767851.
  • 7. Cicirelli F., et al. 2017. “An edge-based platform for dynamic Smart City applications”. Future Generation Computer Systems 76: 106-118. DOI: 10.1016/j.future.2017.05.034.
  • 8. Course-Hero: 3. The Gaussian Kernel. Available at: https://www.coursehero.com/file/12384739/diffusiongaussiankernel/.
  • 9. Cui L., et al. 2018. “A survey on application of machine learning for Internet of Things”. International Journal of Machine Learning and Cybernetics 9(8): 1399-1417. DOI: 10.1007/s13042-018-0834-5.
  • 10. Isaza C., et al. 2019. “Dynamic set point model for driver alert state using digital image processing”. Multimedia Tools and Applications. Multimedia Tools and Applications 78(14): 19543-19563. DOI: 10.1007/s11042-019-7218-z.
  • 11. Krieg J.G., et al. 2018. “Unlocking the smartphone’s sensors for smart city parking”. Pervasive and Mobile Computing 43: 78-95. DOI: 10.1016/j.pmcj.2017.12.002.
  • 12. Muñuzuri Jesús, André Alho, João de Abreu e Silva. 2019. “Evaluating freight loadind/unloading parking zones characteristics, usage and performance in Southern Europe”. European Transport \ Trasporti Europei 73(5). ISSN: 1825-3997.
  • 13. Patkar Manish, Ashish Dhamaniya. 2019. “Effect of on-street parking on effective carriageway width and capacity of urban arterial roads in India”. European Transport \ Trasporti Europei 73(1). ISSN: 1825-3997.
  • 14. Raspberry Pi: Raspberry Pi 3 Model B. Available at: https://www.raspberrypi.org/products/raspberry-pi-3-model-b/.
  • 15. Sobel Irwin, Gary Feldman. 1986. “A 3×3 isotropic gradient operator for image processing”. Stanford Artificial Intelligence Laboratory (SAIL).
  • 16. Tan J.Y., et al. 2017. “GPS-based highway toll collection system: Novel design and operation”. Cogent Engineering 4(1): p 1-10. DOI: 10.1080/23311916.2017.1326199.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-89e15e6b-b0aa-44d8-84bf-1c9895a2b9fe
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