A Study on Car Parking Occupancy Detection Using Smart Camera Networks and Deep Learning

Authors

  • Manisha Sharma Department of Computer Application, Panipat Institute of Engineering And Technology, Samalkha, Kurukshetra University Kurukshetra Author
  • Anshu Sharma Department of Computer Application, Panipat Institute of Engineering And Technology, Samalkha, Kurukshetra University Kurukshetra Author
  • Pooja Dahiya Department of Computer Science and Engineering (Cyber Security), Panipat Institute of Engineering and Technology, Samalkha, Kurukshetra University Kurukshetra Author
  • Komal Malik Department of Computer Science and Engineering, Arya Kanya Mahavidyalaya Gurukul Mor Marja, Karnal, Kurukshetra University Author

DOI:

https://doi.org/10.21467/proceedings.7.6.9

Keywords:

Car Parking Model, Deep Learning Models, Smart Cameras

Abstract

Car parking occupancy detection is a crucial system required at numerous parking facilities. The suggested method is automobile Parking Detection with deep learning. This study aims to address the problem of automobile parking by capturing photos and videos from surveillance cameras to ascertain the occupancy status of parking spaces, determining whether they are empty or occupied. It employs the Convolutional Neural Networks (CNN) algorithm, as it yields the most promising results compared to classic parking detection methods such as Dijkstra's and Ant Colony algorithms. This work uses a smart camera system and deep learning module to improve accuracy of system. The detection is dependable, even when tests are conducted with photos obtained from a perspective distinct from that utilized during training. Furthermore, it exhibits its robustness when training and testing are conducted in various parking lots. As smart city initiatives advance, it is essential for policymakers to stay informed and adapt to the changing technology landscape to cultivate a more livable and resilient urban environment for everyone.

References

[1] A. Dogan, G. Korkmaz, Y. Liu, F. Ozguner, U. Ozguner, K. Redmill, O. Takeshita, and K. Tokuda (2004), Evaluation of intersection collision warning system using an inter-vehicle communication simulator, In Intelligent Transportation Systems, Proceedings. The 7th International IEEE Conference’, pp. 1103–1108.

[2] Aijaz, B. Bochow, F. D6tzer, A. Festag, M. Gerlach, R. Kroh and T. Leinmulller (2006), Attacks on Inter-Vehicle Communication Systems – An Analysis, ‗The 3rd International Workshop on Intelligent Transportation (WIT06), March 2006’, pp. 187-194.

[3] Ahmed Soomro, I., Hasbullah, H., and AbManan, J. L. (2010), ‗Denial of Service (DOS) Attack and Its Possible Solution in VANET.2010‘, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering. World Academy of Science, Engineering and Technology, 4(5), 813-817.

[4] A. Taeihagh and H. S. M. lim, “Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks,” Transport Rev, vol. 39, no. 1, pp. 103–128, JAN. 2019

[5] Anh-Tu Nguyen, “Driver-Automation Cooperative Approach for Shared Steering Control under Multiple System Constraints: Design and Experiments”, IEEE Transactions on Industrial Electronics, 2016.

[6] Akihito Nakamura, “Short-Term Multi-Vehicle Trajectory Planning for Collision Avoidance”, IEEE Transactions on Vehicular Technology, 2020, 1-12.

[7] Britt, J. J. (1994). Case study: Applying formal methods to the traffic alert and collision avoidance system (TCAS) II. Proceedings of the Ninth Annual Conference on Safety, Reliability, Fault Tolerance, Concurrency and Real Time, Security (pp. 39-51). Computer Assurance, 1994. COMPASS'94.

[8] Bianchi, G.,et al. (1996). Performance evaluation and enhancement of the CSMA/CA MAC protocol for 802.11 wireless LANs. Sevnth IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 392-396).

[9] Brenner, P. (1997). A technical tutorial on the IEEE 802.11 protocol. BreezeCom Wireless Communications, 1-24.

[10] Basant Kumar Sahu, “Flocking Control of Multiple AUVs Based on Fuzzy Potential Functions”, IEEE Transactions on Vehicular Technology, 2017, PP.1-15.

[11] Coelingh, E., Jakobsson, L., Lind, H., & Lindman, M. (2007). Collision warning with auto brake: a real-life safety perspective. Innovations for Safety: Opportunities and Challenges., 1-9.

[12] C. Chauvin, S. Lardjane, G. Morel, J.-P. Clostermann, and B. Langard, Human and organizational factors in maritime accidents: Analysis of collisions at sea using the HFACS,” Accident Anal. Prevention, vol. 59, pp. 26–37, Oct. 2013.

[13] C. Tam and R. Bucknall, “Collision risk assessment for ships,” J. Mar. Sci. Technol., vol. 15, no. 3, pp. 257–270, Sep. 2010.

[14] D. Kim, “Lane-Level Localization Using an AVM Camera for an Automated Driving Vehicle in Urban Environments,” IEEE/ASME Trans. Mechatro., vol. 22, no. 1, pp. 280–290, FEB. 2017.

[15] D. K. M. Kufoalor, E. F. Brekke, and T. A. Johansen, “Proactive collision avoidance for ASVs using a dynamic reciprocal velocity obstacles method,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Oct. 2018, pp. 2402–2409.

Downloads

Published

2025-11-21

How to Cite

[1]
M. Sharma, A. Sharma, P. Dahiya, and K. Malik, “A Study on Car Parking Occupancy Detection Using Smart Camera Networks and Deep Learning”, AIJR Proc., vol. 7, no. 6, pp. 68–74, Nov. 2025, doi: 10.21467/proceedings.7.6.9.