Abstract
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.
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Su, B., Lu, S. (2014). A System for Parking Lot Marking Detection. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, CK., Huet, B., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture Notes in Computer Science, vol 8879. Springer, Cham. https://doi.org/10.1007/978-3-319-13168-9_30
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DOI: https://doi.org/10.1007/978-3-319-13168-9_30
Publisher Name: Springer, Cham
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