Research on Parking Space Status Recognition Method Based on Computer Vision

Yongyi Li (), Hongye Mao, Wei Yang, Shuang Guo and Xiaorui Zhang ()
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Yongyi Li: College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China
Hongye Mao: College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China
Wei Yang: College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China
Shuang Guo: Jiangsu Branch, CIECC Urban Construction Design Co., Ltd., Nanjing 210012, China
Xiaorui Zhang: College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China

Sustainability, 2022, vol. 15, issue 1, 1-15

Abstract: To improve the utilization rate of parking space resources and reduce the cost of installing and maintaining sensor recognition, this paper proposed an improved computer vision-based parking space status recognition method. The overall recognition accuracy was improved by graying the video, filtering smoothing noise reduction, image enhancement pre-processing, introducing texture feature extraction method based on LBP operator, improving the background difference method, and then, we used a perceptual hash algorithm to calculate the Hamming distance between the background image and the hash string of the current frame of the video, excluding the influence of light and pedestrian on recognition accuracy. Finally, a parking space status recognition system is developed relied on the Python environment, and parking spaces are recognized in three environmental states: with direct light, without direct light, and in rain and snow. The overall average accuracy of the experimental results was 97.2%, which verifies the accuracy of the model.

Keywords: computer vision; LBP operators; improved background difference method; parking status recognition; threshold (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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