Pedestrian detection systems constitute an important field of research and development in computer vision, specially when applied in protection/safety systems in urban scenarios due to their direct impact in the society, specifically in terms of traffic casualties. In order to face such challenge, this work exploits some developments on statistical machine learning theory, in particular structural risk minimization (SRM) in a cascade ensemble. Namely, the ensemble applies the principle of SRM on a set of linear support vector machines (SVM). The linear SVM complexity, in the Vapnik sense, is controlled by choosing the dimension of the feature space in each cascade stage. To support experimental analysis, a multi-sensor dataset constituted by data from a LIDAR, a monocular camera, an IMU, encoder and a DGPS is introduced in this paper. The dataset, named Laser and Image Pedestrian Detection (LIPD) dataset, was collected in an urban environment, at day light conditions, using an electrical vehicle driven at low speed. Labeled pedestrians and non-pedestrians samples are also available for benchmarking purpose. The cascade of SVMs, trained with image-based features (HOG and COV descriptors), is used to detect pedestrian evidences on regions of interest (ROI) generated by a LIDAR-based processing system. Finally, the paper presents experimental results comparing the performance of a Boosting-SVM cascade and the proposed SRM-SVM cascade classifiers, in terms of detection errors.
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