Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features
Ahrnbom, Martin; Åström, Karl; Nilsson, Mikael (2016-12-16). Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016, 1609 - 1615. 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. Las Vegas, United States: IEEE Computer Society
Conference Proceeding/Paper
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Published
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English
Authors:
Ahrnbom, Martin
;
Åström, Karl
;
Nilsson, Mikael
Department:
Centre for Mathematical Sciences
Mathematics (Faculty of Engineering)
Mathematical Imaging Group
ELLIIT: the Linköping-Lund initiative on IT and mobile communication
eSSENCE: The e-Science Collaboration
Research Group:
Mathematical Imaging Group
Abstract:
In this paper we present a novel, fast and accurate system for detecting the presence of cars in parking lots. The system is based on fast integral channel features and machine learning. The methods are well suited for running embedded on low performance platforms. The methods are tested on a database of nearly 700,000 images of parking spaces, where 48.5% are occupied and the rest are free. The experimental evaluation shows improved robustness in comparison to the baseline methods for the dataset.
Keywords:
Mathematics ;
Computer Vision and Robotics (Autonomous Systems)
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