A cascade classifier applied in pedestrian detection using laser and image-based features

In this paper we present a multistage method applied in pedestrian detection using information from a LIDAR and a monocular-camera mounted on an electric vehicle driving in urban scenarios. The proposed method is a cascade of classifiers trained in two subsets of features, one with laser-based features and the other with a set of image-based features. A specific training approach was developed to adjust the cascade stages in order to enhance the classification performance. The proposed method differs from the conventional cascade regarding the way the selected samples are propagated through the cascade. Thus, the subsequent stages of the proposed cascade receive both negatives and positives from previous ones, relying on a decision margin process. Experiments were conducted in off-line mode, for a set of single component classifiers and for the proposed cascade technique. The results are compared in terms of classification performance metrics and ROC curves.