A System for Parking Lot Marking Detection
In this paper, we proposed a robust parking lot marking detection technique that is one important component for intelligent transportation systems and assisted/autonomous driving. Our system learns features of parking lot markings from training data and matches these templates to detected features in the test video during runtime. In the proposed system, maximally stable extremal regions (MSER) are used to detect a set of parking lot marking candidates. Features are then extracted from the detected candidates and Support Vector Machine (SVM) is applied to classify the parking lot marking in an efficient manner. With the detected parking lot markings, a parking lot is estimated by fitting two adjacent parking lot markings. The proposed technique is tested on real world street-view videos captured with an in-car camera. The experimental results show that the proposed technique is robust and capable of detecting parking lots under different lighting, marking sizes, and marking poses.
KeywordsSupport Vector Machine Local Binary Pattern Maximally Stable Extremal Region Local Binary Pattern Feature Local Binary Pattern Operator
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- 2.Burrow, M.P.N., Evdorides, H.T., Snaith, M.S.: Segmentation algorithms for road marking digital image analysis. In: Proceedings of the Institution of Civil Engineers, Transport, vol. 156, pp. 17–28 (2003)Google Scholar
- 3.Charbonnier, P., Diebolt, F., Guillard, Y., Peyret, F.: Road markings recognition using image processing. In: IEEE Conference on Intelligent Transportation System, ITSC 1997, pp. 912–917 (November 1997)Google Scholar
- 4.Chen, L., Li, Q., Li, M., Mao, Q.: Traffic sign detection and recognition for intelligent vehicle. In: 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 908–913 (June 2011)Google Scholar
- 7.Kiran, C., Prabhu, L., Abdu, R., Rajeev, K.: Traffic sign detection and pattern recognition using support vector machine. In: Seventh International Conference on Advances in Pattern Recognition, ICAPR 2009, pp. 87–90 (February 2009)Google Scholar
- 8.Li, Y., He, K., Jia, P.: Road markers recognition based on shape information. In: 2007 IEEE Intelligent Vehicles Symposium, pp. 117–122 (June 2007)Google Scholar
- 11.Noda, M., Takahashi, T., Deguchi, D., Ide, I., Murase, H., Kojima, Y., Naito, T.: Recognition of road markings from in-vehicle camera images by a generative learning method. In: Proceedings of IAPR Conference on Machine Vision Applications (2009)Google Scholar
- 13.Vacek, S., Schimmel, C., Dillmann, R.: Road-marking analysis for autonomous vehicle guidance. In: Proceedings of the 3rd European Conference on Mobile Robots (2007)Google Scholar
- 14.Veit, T., Tarel, J.P., Nicolle, P., Charbonnier, P.: Evaluation of road marking feature extraction. In: 11th International IEEE Conference on Intelligent Transportation Systems, ITSC 2008, pp. 174–181 (October 2008)Google Scholar
- 15.Wang, X., Han, T., Yan, S.: An hog-lbp human detector with partial occlusion handling. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 32–39 (September 2009)Google Scholar
- 16.Wu, T., Ranganathan, A.: A practical system for road marking detection and recognition. In: 2012 IEEE Intelligent Vehicles Symposium (IV), pp. 25–30 (June 2012)Google Scholar