Abstract: Based on the theory of deep belief network (DBN), a front vehicle detection method by using multisource information is proposed in this paper. Firstly the joint calibration of millimeterwave radar and video camera is conducted and the transformation relation between two sensor coordinate systems is determined. Then through the preprocessing of millimeterwave radar data, the label classification of obstacles is accomplished, and the data of front vehicle objects and other types of obstacles are obtained. Next the data are trained by utilizing DBN and the preliminary identification of front vehicles is performed. Finally the verification windows for front vehicle identification are obtained according to the statistical data on the width and height of common vehicles. Test results show that with the method proposed, the correct rate of front vehicle identification is 912% and the total processing time for single frame image is 37ms, effectively raising the realtime processing speed of the system, in particular, it has good detection results for the vehicles in adverse circumstances like overcast, dark night, light rain, fog or haze, meeting the requirements of accuracy and stability for assisted driving.

Key words: front vehicle detection, deep belief network, multi source information, millimeter wave radar, machine vision