This paper presents a new approach to detect pedestrians using a time-of-flight range camera, for applications in car safety and assistive navigation of the visually impaired. Using 3-D range images not only enables fast and accurate object segmentation and but also provides useful information such as distances to the pedestrians and their probabilities of collision with the user. In the proposed approach, a 3-D range image is first segmented using a modified local-variation algorithm. Three state-of-the-art feature extractors (GIST, SIFT, and HOG) are then used to find shape features for each segmented object. Finally, the SVM is applied to classify objects into pedestrian or non-pedestrian. Evaluated on an image data set acquired using a time-of-flight camera, the proposed approach achieves a classification rate of 95.0%.
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