Pedestrian recognition using combined low-resolution depth and intensity images

We present a novel system for pedestrian recognition through depth and intensity measurements. A 3D-Camera is used as main sensor, which provides depth and intensity measurements with a resolution of 64 times 8 pixels and a depth range of 0-20 meters. The first step consists of extracting the ground plane from the depth image by an adaptive flat world assumption. An AdaBoost head-shoulder detector is then used to generate hypotheses about possible pedestrian positions. In the last step every hypothesis is classified with AdaBoost or a SVM as pedestrian or non-pedestrian. We evaluated a number of different features known from the literature. The best result was achieved by Fourier descriptors in combination with the edges of the intensity image and an AdaBoost classifier, which resulted in a recognition rate of 83.75 percent.