Camera-light detection and ranging data fusion-based system for pedestrian detection

Abstract. A hybrid system for pedestrian detection is proposed, in which both visible camera and light detection and ranging (LIDAR) data of the same scene are used. The proposed method is achieved in two main stages. First, the 3-D LIDAR points are clustered and the resulting clusters are projected onto the visible image to get regions of interests (ROIs). A postprocessing method is applied on the resulting ROIs to keep only significant ones. Second, a hybrid feature is defined to classify the ROIs extracted from the first stage. It involves a combination of visual (camera) and range (LIDAR)-based features. The visual feature is a combination of the so-called HOG-Color, which is an extension of the classical histogram of oriented gradients (HOG) feature to the RGB color space and the local self-similarity feature. The range feature we define is called histogram of dominant silhouette orientation descriptor. It is computed from the depth values of the pixels belonging to the ROI. Random forest, support vector machine, and Adaboost classifiers have been tested together with the introduced hybrid feature and the first one is adopted as it outperforms the two other ones. The proposed method has been tested on KITTI dataset and the results are satisfactory when compared to recent state-of-the-art methods.