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.
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Dornaika, F., Hammoudi, K., Melkemi, M. et al. An efficient pyramid multi-level image descriptor: application to image-based parking lot monitoring. SIViP 13, 1611–1617 (2019). https://doi.org/10.1007/s11760-019-01512-6
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DOI: https://doi.org/10.1007/s11760-019-01512-6