Moving objects detection and recognition using sparse spatial information in urban environments

Moving objects detection and recognition around an intelligent vehicle are active research fields. A great number of approaches have been proposed in recent decades. This paper proposes a novel approach based solely on spatial information to solve this problem. Moving objects detection is achieved in conjunction with an egomotion estimation by sparse matched feature points. For objects recognition, we firstly present a method to boost simple spatial information by Kernel Principal Component Analysis (KPCA). Then, two kinds of classifiers (Random Forest and Gradient Boosting Trees) are trained offline to recognize several common categories of moving objects in urban scenarios (vehicle, pedestrian, cyclist, ...). Experiments are implemented and the results confirm the effectiveness of the proposed algorithm. Furthermore, a comparison to a previous similar method is performed to verify the enhancement of classification by the advanced spatial features.