Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithmsTools 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. Full text not available from this repository.AbstractThe 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.
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