machine perceiving smart vehicle parking detection using image processing system

SRINIVASAN K,VARADHARAJAN K,S.RATHANA SABAPATHY B.E.,M.Tech.,

Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology

ISSN: 2321-3337          Impact Factor:1.521         Volume:6         Issue:3         Year: 30 March,2021         Pages:1420-1425

International Journal of Advanced Research in Computer Science Engineering and Information Technology

Abstract

Scope of this project is to provide a safe working environment to the drivers. The driver will get frustrated for waiting for allocation of parking space. Our project will help to identify the exact parking area without any difficulties by sending SMS to the specific driver. In our project we can able to view the parking area using CCTV Footages. The scope of future enhancement in smart parking system for improved efficiency in allocating a particular slot & intimate the information to the particular driver. improvement is identifying injuries for multiple person at a time and giving solutions. In some of the parking areas are lacking such facilities and hence fail all the security norms necessary to park a vehicle. By looking such a huge concern it is highly required that each and every parking areas should be well equipped with high tech parking control systems, that nevertheless lasts the best. These innovative parking control systems not only make a bright choice but also allow you to pay the right price without getting any worry.

Kewords

CNN – Convolutional Neural Network, GPU – Graphical Processing Unit, GUI – Graphical User Interface

Reference

1. Donald. C. Shoup, “The High Cost of Free Parking”, Chicago:Planners Press, American Planning Association, 2017. 2. Praveen Meduri, and Eric Telles "A Haar-Cascade classifier based Smart Parking System. International Conference. IP, Comp. Vision, and Pattern Recognition. 2018. 3. Gulraiz Khan, Zeeshan Tariq, and Muhammad Usman Ghani Khan. Multi- person tracking based on faster r-cnn and deep appearance features. In Visual Object Tracking in the Deep Neural Networks Era. IntechOpen, 2019. 4. A. Awan S. Agarwal and D. Roth. UIUC Image Database for CarDetection. http: //cogcomp.cs.illinois.edu/Data/Car/, 2004. [Online; accessed 29-April-2019]. 5. T. Lin, H. Rivano and F. Le Mouël, "A Survey of Smart Parking Solutions," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 12, pp. 3229-3253, Dec. 2017. 6. R. Grodi, D. B. Rawat and F. Rios- Gutierrez, "Smart parking: Parking occupancy monitoring and visualization system for smart cities," SoutheastCon 2016, Norfolk, VA, 2016, pp. 1-5.