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Signal, Image and Video Processing

, Volume 13, Issue 8, pp 1611–1617 | Cite as

An efficient pyramid multi-level image descriptor: application to image-based parking lot monitoring

  • F. DornaikaEmail author
  • K. Hammoudi
  • M. Melkemi
  • T. D. A. Phan
Original Paper
  • 61 Downloads

Abstract

Efficient analysis of parking slot occupancy is still a complex task in reason of the variety of slot textures and of the difficulty to characterize the relevant information of their associated images. In this paper, we propose a handcrafted approach supported by machine learning techniques. The two main contributions are as follows: Firstly, we introduce a compact handcrafted image descriptor, named pyramid multi-level descriptor (PMLD), designed to capture features at different scales and at different receptive fields in the image region. Secondly, we provide a comparative study of several popular image-based handcrafted and deep learning features. Experiments are conducted on two public datasets: PKLot and CNRPark. It follows that PMLD achieves better results than classical handcrafted descriptors and achieves similar results to those obtained by transfer learning of the deep CNN VGG-F.

Keywords

Parking lot monitoring Handcrafted image features Multi-scale representation Transfer learning Classification Performance evaluation 

Notes

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of the Basque Country UPV/EHUSan SebastianSpain
  2. 2.IKERBASQUE, Basque Foundation for ScienceBilbaoSpain
  3. 3.IRIMASUniversité de Haute-AlsaceMulhouseFrance
  4. 4.Université de StrasbourgStrasbourgFrance

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