Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms
The time-consuming search for parking lots could be assisted by efficient routing systems. Still, the needed vacancy detection is either very hardware expensive, lacks detail or does not scale well for industrial application. This paper presents a video-based system for cost-effective detection of v...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2015
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| Online Access: | https://eprints.nottingham.ac.uk/35406/ |
| _version_ | 1848795069506650112 |
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| author | Tschentscher, Marc Koch, Christian König, Markus Salmen, Jan Schlipsing, Marc |
| author_facet | Tschentscher, Marc Koch, Christian König, Markus Salmen, Jan Schlipsing, Marc |
| author_sort | Tschentscher, Marc |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The time-consuming search for parking lots could be assisted by efficient routing systems. Still, the needed vacancy detection is either very hardware expensive, lacks detail or does not scale well for industrial application. This paper presents a video-based system for cost-effective detection of vacant parking lots, and an extensive evaluation with respect to the system’s transferability to unseen environments. Therefore, different image features and learning algorithms were examined on three independent datasets for an unbiased validation. A feature / classifier combination which solved the given task against the background of a robustly scalable system, which does not require re-training on new parking areas, was found. In addition, the best feature provides high performance on gray value surveillance cameras. The final system reached an accuracy of 92.33% to 99.96%, depending on the parking rows’ distance, using DoG-features and a support vector machine. |
| first_indexed | 2025-11-14T19:26:14Z |
| format | Conference or Workshop Item |
| id | nottingham-35406 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:26:14Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-354062020-05-04T17:12:52Z https://eprints.nottingham.ac.uk/35406/ Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms Tschentscher, Marc Koch, Christian König, Markus Salmen, Jan Schlipsing, Marc The time-consuming search for parking lots could be assisted by efficient routing systems. Still, the needed vacancy detection is either very hardware expensive, lacks detail or does not scale well for industrial application. This paper presents a video-based system for cost-effective detection of vacant parking lots, and an extensive evaluation with respect to the system’s transferability to unseen environments. Therefore, different image features and learning algorithms were examined on three independent datasets for an unbiased validation. A feature / classifier combination which solved the given task against the background of a robustly scalable system, which does not require re-training on new parking areas, was found. In addition, the best feature provides high performance on gray value surveillance cameras. The final system reached an accuracy of 92.33% to 99.96%, depending on the parking rows’ distance, using DoG-features and a support vector machine. 2015-07-17 Conference or Workshop Item PeerReviewed Tschentscher, Marc, Koch, Christian, König, Markus, Salmen, Jan and Schlipsing, Marc (2015) Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms. In: 2015 International Joint Conference on Neural Networks (IJCNN), 12-17 July 2015, Killarney, Ireland. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7280319 10.1109/IJCNN.2015.7280319 10.1109/IJCNN.2015.7280319 10.1109/IJCNN.2015.7280319 |
| spellingShingle | Tschentscher, Marc Koch, Christian König, Markus Salmen, Jan Schlipsing, Marc Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms |
| title | Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms |
| title_full | Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms |
| title_fullStr | Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms |
| title_full_unstemmed | Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms |
| title_short | Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms |
| title_sort | scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms |
| url | https://eprints.nottingham.ac.uk/35406/ https://eprints.nottingham.ac.uk/35406/ https://eprints.nottingham.ac.uk/35406/ |