Object segmentation and classification using 3-D range camera
This paper proposes a vision system using a 3-D range camera for scene segmentation and pedestrian classification. The system detects and segments objects in the foreground, measures their distances to the camera, and classifies them into pedestrians and non-pedestrian obstacles. Combining range and intensity images enables fast and accurate object segmentation, and provides useful navigation cues such as the range and type of nearby objects and the ground surface. In the proposed approach, a 3-D range image is segmented using histogram processing and mean-shift clustering. The ground surface is detected by estimating its normal vector in 3-D space. Fourier and GIST descriptors are then applied on each detected region to extract shape and texture features. Finally, support vector machines are used to classify objects; in this paper we focus on differentiating pedestrian and non-pedestrian regions. The performance of the proposed system is evaluated with two datasets. One dataset for object segmentation and pedestrian classification is acquired by us using a 3-D range camera; the other is a public RGB-D dataset for people detection. Experimental results show that the proposed system performs favorably compared to some existing segmentation and feature extraction approaches.