A Systematic Review of Machine-vision-based Smart Parking Systems

Muhammad Zainal Abidin, Reza Pulungan

Abstract


The development of smart city concept, particularly in smart parking systems, has not solved a problem that occurs in metropolitan areas, such as in urban areas where the population has continued to rise, resulting in high demand for private vehicles and parking spaces. Finding a parking space is known as the most common issue the drivers have had specifically on peak hours’ time. During peak hours, the difficulty arises as many people look around to find vacant parking space at once, which causes many negative impacts on cities and drivers themselves, such as pollution, traffic congestion, traffic accidents, waste of time and fuel, emotions and so on. As a solution, smart parking system exist to equip parking lots with many different types of sensors to automatically detect free parking space that would guide drivers to find the nearest car parking space as efficient as possible. An effective smart parking system can solve this problem and make better use of parking resources. However, many smart parking systems still uses embedded sensors that are expensive for installation and inefficient. This paper presents a review of the existing approaches to the smart parking system. This paper focuses on a machine-vision-based technology used for smart parking system and highlights its main features, advantages and disadvantages.


Keywords


Smart Parking System; Machine Vision; Image Processing; Parking Space Detection; Vehicle Detection; Video Processing

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References


Paidi, V., Fleyeh, H., Håkansson, J., & Nyberg, R. G. (2018). Smart Parking Tools Suitability for Open Parking Lots: A Review. In 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018) (pp. 600-609). Madeira, Portugal: SciTePress.

Enrìquez, F., Soria, L. M., & Álvarez-Garcìa, J. A. (2017). Existing Approaches to Smart Parking: An Overview. In International Conference on Smart Cities (Smart-CT 2017), LNCS 10268, (pp. 63–74). Malaga, Spain: Springer.

Moses, N., & Chincholkar, Y. D. (2016). Smart Parking System for Monitoring Vacant Parking. International Journal of Advanced Research in Computer and Communication Engineering, 5(6), 717-720.

Hakim, I. M., Christover, D., & Marinda, A. M. J. (2019). Implementation of an Image Processing Based Smart Parking System Using Haar-Cascade Method. In 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE), (pp. 222-227). Malaysia: IEEE.

Hilmani, A., Maizate, A., & Hassouni, L. (2018). Designing and Managing Smart Parking System Using Wireless Sensor Networks. Journal of Sensor and Actuator Networks, 7(2): 24.

Zajam, A., & Dholay, S. (2018). Detecting Efficient Parking Space Using Smart Parking. In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), (pp.1-7). Bangalore, India: IEEE.

Zacepins, A., Komasilovs, V. & Kviesis, A. (2018). Implementation of Smart Parking Solution by Image Analysis. In 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018) (pp. 666-669). Madeira, Portugal: SciTePress.

Choeychuen, K. (2013). Automatic Parking Lot Mapping for Available Parking Space Detection. In 5th International Conference on Knowledge and Smart Technology (KST), (pp. 117-121). Chonburi, Thailand: IEEE.

Almeida, P., Oliveira, L. S., Silva Jr., E., Britto Jr., A. & Koerich, A. (2013). Parking Space Detection using Textual Descriptors. In 2013 IEEE International Conference on Systems, Man, and Cybernetics, (pp. 3603-3608). Manchester, UK: IEEE.

Lin, T., Rivano, H., & Le Mouёl, F. (2017). A Survey of Smart Parking Solutions. IEEE Transactions on Intelligent Transportation Systems, 18(12), 3229-3253.

Amato, G., Carrara, F., Falchi, F., Gennaro, C., & Vairo, C. (2016). Car Parking Occupancy Detection Using Smart Camera Networks and Deep Learning. In 2016 IEEE Symposium on Computers and Communication (ISCC), (pp. 1212-1217). Mersina, Italy: IEEE.

Bin, Z., Dalin, J., Fang, W., & Tingting, W. (2009). A Design of Parking Space Detector Based on Video Image. In 2009 9th International Conference on Electronic Measurement & Instruments, (pp. 253-256). Beijing, China: IEEE.

Yusnita, R., Norbaya, F. & Basharuddin, N. (2012). Intelligent Parking Space Detection System Based on Image Processing. International Journal of Innovation, Management and Technology, 3(3), 232-235.

Al-Kharusi, H., & Al-Bahadly, I. (2014). Intelligent Parking Management System Based on Image Processing. World Journal of Engineering and Technology, 2, 41-53.

Fraifer, M., & Fernström, M. (2017). Designing a Smart Car Parking System (PoC) Prototype Utilizing CCTV Nodes: A Vision of an IoT Parking System via UCD Process. Advances in Science, Technology and Engineering Systems Journal, 2(3), 755-764.

Kurniawan, A. (2017). Intelligent IoT Projects in 7 Days. Birmingham: Packt Publishing.

Kommey, B., Addo, E. O., & Agbemenu, A. S. (2018). A Smart Image Processing-based System for Parking Space Vacancy Management. International Journal of Computer Applications, 182(5), 1-6.

Loong, D. N. C., Isaak, S., & Yusof, Y. (2019). Machine Vision-based Smart Parking System Using Internet of Things. TELKOMNIKA, 17(4), 2098-2106.

Di Mauro, D., Furnari, A., Patanѐ, G., Battiato, S., & Farinella, G. M. (2019). Estimating the Occupancy Status of Parking Areas by Counting Cars and Non-empty Stalls. Journal of Visual Communication and Image Representation, 62, 234–244.

Trivedi, J. D., Devi, M. S., & Dave. D. H. (2020). Different Modules for Car Parking System Demonstrated Using Hough Transform for Smart City Development. In the International Conference on Intelligent Manufacturing and Energy Sustainability (ICIMES 2019), (pp 109-121). Hyderabad, India: Springer.

Polycarpou, E., Lambrinos, L., & Protopapadakis, E. (2013). Smart Parking Solutions for Urban Areas. In 2013 IEEE 14th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), (pp. 1-6). Madrid, Spain: IEEE.




DOI: https://doi.org/10.15294/sji.v7i2.25654

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