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Proper management of parking spaces is essential in urban environments. This study proposes an approach for parking space segmentation using the K-means algorithm and the OpenCV library. The main objective is to determine the trapezoid describing the parking area by analyzing data previously collected from multiple photographs. These images contain several vehicles parked in different dispositions and moments in time. For this, the coordinates of the four leading edges that compose each car were considered. The previously obtained data were used to estimate the trapezoid defining each photograph’s parking zone. This approach combines segmentation and image processing techniques to delimit parking spaces in urban environments.

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Automatic Parking Space Segmentation Using K-Means Clustering and Image Processing Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Anthony Xavier Romero Gonzalez,
  • Kevin Sebastian Campoverde Ambrosi,
  • Patricio Eduardo Ramon Celi,
  • Alexandra Bermeo,
  • Marcos Orellana,
  • Jorge Luis Zambrano-Martinez,
  • Patricio Santiago García-Montero

摘要

Proper management of parking spaces is essential in urban environments. This study proposes an approach for parking space segmentation using the K-means algorithm and the OpenCV library. The main objective is to determine the trapezoid describing the parking area by analyzing data previously collected from multiple photographs. These images contain several vehicles parked in different dispositions and moments in time. For this, the coordinates of the four leading edges that compose each car were considered. The previously obtained data were used to estimate the trapezoid defining each photograph’s parking zone. This approach combines segmentation and image processing techniques to delimit parking spaces in urban environments.