Multiclass obstacles detection and classification using stereovision and Bayesian network for intelligent vehicles

Abstract

Intelligent vehicles should be able to detect various obstacles and also identify their types so that the vehicles can take an appropriate level of protection and intervention. This article presents a method of detecting and classifying multiclass obstacles for intelligent vehicles. A stereovision-based method is used to segment obstacles from traffic background and measure three-dimensional geometrical features. A Bayesian network (BN) model has been established to further classify them into five classes, including pedestrian, cyclist, car, van, and truck. The BN model is trained using substantial data samples. The optimized structure of the model is determined from the necessary path condition method with a presupposition constraint (NPC+PC). The conditional probability table of the discrete nodes and the conditional probability distribution of the continuous nodes are determined from expectation maximization (EM) training algorithm with consideration of prior domain knowledge. Experiments were conducted using the object detection data set on the public KITTI benchmark, and the results show that the proposed BN model exhibits an excellent performance for obstacle classification while the full pipeline of the method including detection and classification is in the upper middle level compared with other existing methods.