Evaluation of parking space detection systems using wireless cameras

  • David Lima IFAL
  • Eliana Almeida UFAL
  • Andre Aquino UFAL


This work presents a deep analysis of na embedded system to detect free on-street parking slots by using wireless cameras. To allow distributed processing and the communication, intelligent boards were embedded in the cameras. Three different system architectures are considered: sentralized, hybrid and embedded. Each architeture was simulated considering the variation of the communications radius size, the amount of cameras and the amount of concurrent system queries. The results of simulation revealed that the performance of embedded proposal is better than the hybrid one in all scenarios. Additionaly, the embedded proposal was evaluated considering eventual cameras failure. It was observed that these failures influence directly the answer time of system.


Al-Kharusi, H. and Al-Bahadly, I. (2014). Intelligent parking management system based on image processing. World Journal of Engineering and Technology, 2:55-67.

Alhammad, A., Siewe, F., and Al-Bayatti, A. H. (2012). An infostation-based context-aware on-street parking system. In International Conference on Computer Systems and Industrial Informatics.

Geng, Y. and Cassandras, C. G. (2012). A new "smart parking" system infrastructure and implementation. Procedia - Social and Behavioral Sciences, 54:1278-1287.

Lima, D. H., Aquino, A. L., Ramos, H. S., Almeida, E. S., and Rodrigues, J. J. (2014). Oasys: An opportunistic and agile system to detect free on-street parking using intel-ligent boards embedded in surveillance cameras. Journal of Network and Computer Applications, 46(0):241 — 249.

Maia, G., Aquino, A. L. L., Guidoni, D. L., and Loureiro, A. A. F. (2013). A multicast reprogramming protocol for wireless sensor networks based on small world concepts. Journal of Parallel and Distributed Computing, 73(9):1277-1291.

Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Trans-actions on Systems, Man and Cybernetics, 9(1):62-66.

Prewitt, J. M. S. (1970). Object enhancement and extraction. Picture Processing and Psychopictorics,75:75 — 149.

Reddy, P. S., Naveen Kumar, G. S., Reddy, B. R., H., S. C., and Abhilash, K. B. (2013). Intelligent parking space detection system based on image segmentation. International Journal for Scientific Reseandt and Development, 1(6): 1310-1312.

Sanchez, L. (2010). Smartsantander: Experimenting the future internet in the city of the future. In 21 th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

Stehman. S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1):77-89.

Thornton, D. A., Redmill, K, and Coifman, B. (2014). Automated parking surveys from a LIDAR equipped vehicle. Transportation Research Rut C: Emerging Technologies, 39(0):23-35.

Zhang, Z., Li, X., Yuan, H., and Yu, F. (2013). A street parking system using wireless sensor networks. International Journal of Distributed Sensor Networks, 2013:1-10.

LIMA, David; ALMEIDA, Eliana; AQUINO, Andre. Evaluation of parking space detection systems using wireless cameras. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 7. , 2015, Recife. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2015 . p. 181-190. ISSN 2595-6183. DOI: https://doi.org/10.5753/sbcup.2015.10181.