Vacant parking space detection based on plane-based bayesian hierarchical framework

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37 Scopus citations


In this paper, we propose a vacant parking space detection system that operates day and night. In the daytime, the major challenges of the system include dramatic lighting variations, shadow effect, inter-object occlusion, and perspective distortion. In the nighttime, the major challenges include insufficient illumination and complicated lighting conditions. To overcome these problems, we propose a plane-based method which adopts a structural 3-D parking lot model consisting of plentiful planar surfaces. The plane-based 3-D scene model plays a key part in handling inter-object occlusion and perspective distortion. On the other hand, to alleviate the interference of unpredictable lighting changes and shadows, we propose a plane-based classification process. Moreover, by introducing a Bayesian hierarchical framework to integrate the 3-D model with the plane-based classification process, we systematically infer the parking status. Last, to overcome the insufficient illumination in the nighttime, we also introduce a preprocessing step to enhance image quality. The experimental results show that the proposed framework can achieve robust detection of vacant parking spaces in both daytime and nighttime.

Original languageEnglish
Article number6488793
Pages (from-to)1598-1610
Number of pages13
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number9
StatePublished - 17 Sep 2013


  • Bayesian inference
  • histogram of oriented gradients
  • image classification
  • parking space detection

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