Development of an Open-Space Visual Smart Parking System

Parking has become an evident road traffic problem arising from the increase in the use of cars. To solve the continuous waste of time and energy in search of parking lots in the country, we have developed a visual smart parking monitoring system for open-space parking lots. The system comprises of wireless sensor nodes equipped with cameras that monitor the status of each parking space in a parking lot. Each node is capable of running a parking lot occupancy algorithm to check for occupancies. Finally, the results are sent to a central controller connected to the internet, which will then be disseminated through a GUI made for both web and mobile platforms. The system was proven to have a potential for robustness, energy-efficiency, and further improvement.

[1]  Luiz Eduardo Soares de Oliveira,et al.  PKLot - A robust dataset for parking lot classification , 2015, Expert Syst. Appl..

[2]  Andrew Zisserman,et al.  Metric rectification for perspective images of planes , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[3]  Martin Reisslein,et al.  Towards Efficient Wireless Video Sensor Networks: A Survey of Existing Node Architectures and Proposal for A Flexi-WVSNP Design , 2011, IEEE Communications Surveys & Tutorials.

[4]  Ammad Ali,et al.  Face Recognition with Local Binary Patterns , 2012 .

[5]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[6]  T. Sugimoto,et al.  Parking guidance and information systems , 1995, Pacific Rim TransTech Conference. 1995 Vehicle Navigation and Information Systems Conference Proceedings. 6th International VNIS. A Ride into the Future.

[7]  Saurabh Chatterjee,et al.  The 3-Point Method: A Fast, Accurate and Robust Solution to Vanishing Point Estimation , 2013 .

[8]  Marco Tagliasacchi,et al.  A visual sensor network for parking lot occupancy detection in Smart Cities , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

[9]  Huadong Ma,et al.  Robust Head-Shoulder Detection by PCA-Based Multilevel HOG-LBP Detector for People Counting , 2010, 2010 20th International Conference on Pattern Recognition.

[10]  D. Shoup Cruising for Parking , 2006 .

[11]  A. Conci,et al.  Robust background subtraction on traffic videos , 2011, 2011 18th International Conference on Systems, Signals and Image Processing.

[12]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).