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.
|Publication Date||Jul 17, 2015|
|Peer Reviewed||Peer Reviewed|
|Book Title||2015 International Joint Conference on Neural Networks (IJCNN)|
|APA6 Citation||Tschentscher, M., Koch, C., König, M., Salmen, J., & Schlipsing, M. (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). https://doi.org/10.1109/IJCNN.2015.7280319|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf|
|Additional Information||© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
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