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Scene segmentation and pedestrian classification from 3-D range and intensity images

Conference Paper


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Abstract


  • This paper proposes a new approach to classify obstacles using a time-of-flight camera, for applications in assistive navigation of the visually impaired. Combining range and intensity

    images enables fast and accurate object segmentation, and provides useful navigation cues such as distances to the nearby obstacles and obstacle types. In the proposed approach, a 3-D range image is first segmented using histogram thresholding and mean-shift grouping. Then Fourier and GIST descriptors are applied on each segmented object to extract shape and texture features. Finally, support vector machines are used to recognize the obstacles. This paper focuses on classifying pedestrian and non-pedestrian obstacles. Evaluated on an image data set acquired using a time-of-flight camera, the proposed approach achieves a classification rate of 99.5%.

Publication Date


  • 2012

Citation


  • Wei, X., Phung, S. Lam. & Bouzerdoum, A. (2012). Scene segmentation and pedestrian classification from 3-D range and intensity images. ICME 2012: IEEE International Conference on Multimedia and Expo (pp. 103-108). USA: IEEE.

Scopus Eid


  • 2-s2.0-84868138903

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1254&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/249

Start Page


  • 103

End Page


  • 108

Place Of Publication


  • http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6298382&isnumber=6298237

Abstract


  • This paper proposes a new approach to classify obstacles using a time-of-flight camera, for applications in assistive navigation of the visually impaired. Combining range and intensity

    images enables fast and accurate object segmentation, and provides useful navigation cues such as distances to the nearby obstacles and obstacle types. In the proposed approach, a 3-D range image is first segmented using histogram thresholding and mean-shift grouping. Then Fourier and GIST descriptors are applied on each segmented object to extract shape and texture features. Finally, support vector machines are used to recognize the obstacles. This paper focuses on classifying pedestrian and non-pedestrian obstacles. Evaluated on an image data set acquired using a time-of-flight camera, the proposed approach achieves a classification rate of 99.5%.

Publication Date


  • 2012

Citation


  • Wei, X., Phung, S. Lam. & Bouzerdoum, A. (2012). Scene segmentation and pedestrian classification from 3-D range and intensity images. ICME 2012: IEEE International Conference on Multimedia and Expo (pp. 103-108). USA: IEEE.

Scopus Eid


  • 2-s2.0-84868138903

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1254&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/249

Start Page


  • 103

End Page


  • 108

Place Of Publication


  • http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6298382&isnumber=6298237