Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICASSP.2008.4518055
Title: A Bayesian hierarchical detection framework for parking space detection
Authors: Huang C.-C.
Wang S.-J.
Chang Y.-J.
Chen T. 
Keywords: Bayesian framework
Graphical models
Optimization
Segmentation
Semantic detection
Issue Date: 2008
Citation: Huang C.-C., Wang S.-J., Chang Y.-J., Chen T. (2008). A Bayesian hierarchical detection framework for parking space detection. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings : 2097-2100. ScholarBank@NUS Repository. https://doi.org/10.1109/ICASSP.2008.4518055
Abstract: In this paper, a 3-layer Bayesian hierarchical detection framework (BHDF) is proposed for robust parking space detection. In practice, the challenges of the parking space detection problem come from luminance variations, inter-occlusions among cars, and occlusions caused by environmental obstacles. Instead of determining the status of parking spaces one by one, the proposed BHDF framework models the inter-occluded patterns as semantic knowledge and couple local classifiers with adjacency constraints to determine the status of parking spaces in a row-by-row manner. By applying the BHDF to the parking space detection problem, the available parking spaces and the labeling of parked cars can be achieved in a robust and efficient manner. Furthermore, this BHDF framework is generic enough to be used for various kinds of detection and segmentation applications.
Source Title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/146238
ISBN: 1424414849
9781424414840
ISSN: 15206149
DOI: 10.1109/ICASSP.2008.4518055
Appears in Collections:Staff Publications

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