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A System for Parking Lot Marking Detection

  • Bolan Su
  • Shijian Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8879)

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.

Keywords

Support Vector Machine Local Binary Pattern Maximally Stable Extremal Region Local Binary Pattern Feature Local Binary Pattern Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bolan Su
    • 1
  • Shijian Lu
    • 1
  1. 1.Institute for Infocomm ResearchSingapore

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