Abstract-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 64x8 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.
Abstract. We present a novel pedestrian detection system based on probabilistic component assembly. A part-based model is proposed which uses three parts consisting of head-shoulder, torso and legs of a pedestrian. Components are detected using histograms of oriented gradients and Support Vector Machines (SVM). Optimal features are selected from a large feature pool by boosting techniques, in order to calculate a compact representation suitable for SVM. A Bayesian approach is used for the component grouping, consisting of an appearance model and a spatial model. The probabilistic grouping integrates the results, scale and position of the components. To distinguish both classes, pedestrian and non-pedestrian, a spatial model is trained for each class. Below miss rates of 8% our approach outperforms state of the art detectors. Above, performance is similar.
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