Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2010.16.12.1226

Door Detection with Door Handle Recognition based on Contour Image and Support Vector Machine  

Lee, Dong-Wook (Korea University)
Park, Joong-Tae (Korea University)
Song, Jae-Bok (Korea University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.16, no.12, 2010 , pp. 1226-1232 More about this Journal
Abstract
A door can serve as a feature for place classification and localization for navigation of a mobile robot in indoor environments. This paper proposes a door detection method based on the recognition of various door handles using the general Hough transform (GHT) and support vector machine (SVM). The contour and color histogram of a door handle extracted from the database are used in GHT and SVM, respectively. The door recognition scheme consists of four steps. The first step determines the region of interest (ROI) images defined by the color information and the environment around the door handle for stable recognition. In the second step, the door handle is recognized using the GHT method from the ROI image and the image patches are extracted from the position of the recognized door handle. In the third step, the extracted patch is classified whether it is the image patch of a door handle or not using the SVM classifier. The door position is probabilistically determined by the recognized door handle. Experimental results show that the proposed method can recognize various door handles and detect doors in a robust manner.
Keywords
door detection; door handle recognition; general hough transform; support vector machine;
Citations & Related Records

Times Cited By SCOPUS : 1
연도 인용수 순위
  • Reference
1 6] J. Lee, N. L. Doh, W. K. Chung, B. You, and Y. I. Youm, “Door detection algorithm of mobile robot in hallway using pccamera,” International Symposium on Automation and Robotics in Construction, 2004.
2 D. H. Ballard, “Generalizing the hough transform to detect arbitrary shapes,” Pattern Recognition, vol. 13, pp. 111-122, 1980.
3 V. Vapnik, Statistical Learning Theory, John Wiley and Sons, 1998.
4 S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, The MIT Press, 2005.
5 I. Monasterio and W. Burgard, “Mobile robot mapping and localization in non-static environments,” Proc. of the National Conf. on Artificial Intelligence, July 2005.
6 O. M. Mozos, P. Jensfelt, H. Zender, G.-J. M. Kruijff, and W. Burgard, “From labels to semantics: An integrated system for conceptual spatial representations of indoor environments for mobile robots,” Proc. of the IEEE/RSJ Intelligent Robots and Systems, Apr. 2007.
7 C. Stachniss and E. Lazkano, “Learning to traverse doors using visual information,” Mathematics and Computers in Simulation, vol. 60, no. 3, pp. 347-356, 2002.   DOI
8 J. J. Guerrero, A. C. Murillo, J. Kosecka, and C. Sagues, “Visual door detection integrating appearance and shape cues,” Robotics and Autonomous Systems, vol. 56, no. 6, pp. 512-521, 2008.   DOI
9 S. Stoeter and L. Papanikolopoulos, “Real-time door detection in cluttered environments,” Proc. of the IEEE International Symposium on Intelligent Control, July 2000.