Hardware-friendly pedestrian detection and impact prediction

We present a system for pedestrian detection and impact prediction, from a frontal camera situated on a moving vehicle. The system combines together the output of several algorithms to form a reliable detection and positioning of pedestrians. One of the important contributions of this paper is a highly-efficient algorithm for classification of pedestrian images using a learned set of features, each feature based on a 5/spl times/5 pixels shape. The learning of the features is done using AdaBoost and genetic-like algorithms. The described application was developed as a part of the CAMELLIA project, thus all the algorithms used in this application are designed to use a special set of low level image processing operations provided by the smart imaging core developed in the project. Fusion of the various algorithms results and tracking of pedestrians is done using particle filtering, providing a good tool to predict the future movement of pedestrians, in order to estimate impact probability.