Scalable real-time parking lot classification: An evaluation of image features and supervised learning algorithms

Marc Tschentscher, Christian Koch, Markus Konig, Jan Salmen, Marc Schlipsing
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
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
more » ... 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. A. Sensor-based methods Concerning the sensor installation procedure, the sensorbased systems can be divided into two categories: intrusive and non-intrusive sensor systems. While intrusive sensors are typically installed in the surface, by tunnelling under the surface or anchoring to the surface, leading to invasive installation and maintenance procedures as well as traffic
doi:10.1109/ijcnn.2015.7280319 dblp:conf/ijcnn/TschentscherKKS15 fatcat:w4qhfm57mverraypwadjdzifa4