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

Authors

Xue Wei, University of WollongongFollow
Son Lam Phung, University of WollongongFollow
Abdesselam Bouzerdoum, University of WollongongFollow

RIS ID

70811

Publication Details

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

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%.