Car parking occupancy detection using smart camera networks and Deep Learning

  • Amato G
  • Carrara F
  • Falchi F
 et al. 
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Abstract

This paper presents an approach for real-time car parking occupancy detection that uses a Convolutional Neural Network (CNN) classifier running on-board of a smart camera with limited resources. Experiments show that our technique is very effective and robust to light condition changes, presence of shadows, and partial occlusions. The detection is reliable, even when tests are performed using images captured from a viewpoint different than the viewpoint used for training. In addition, it also demonstrates its robustness when training and tests are executed on different parking lots. We have tested and compared our solution against state of the art techniques, using a reference benchmark for parking occupancy detection. We have also produced and made publicly available an additional dataset that contains images of the parking lot taken from different viewpoints and in different days with different light conditions. The dataset captures occlusion and shadows that might disturb the classification of the parking spaces status.

Author-supplied keywords

  • Automobiles
  • CNN classifier
  • Classification
  • Computer architecture
  • Convolutional Neural Networks
  • Deep Learning
  • Machine Learning
  • Machine learning
  • Monitoring
  • Smart cameras
  • Training
  • cameras
  • convolutional neural network classifier
  • image capture
  • image classification
  • learning (artificial intelligence)
  • light condition
  • lighting
  • neural nets
  • parking lots
  • parking space status classification
  • partial occlusions
  • real-time car parking occupancy detection
  • traffic engineering computing

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Authors

  • G Amato

  • F Carrara

  • F Falchi

  • C Gennaro

  • C Vairo

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