Robust Moving Object Detection Using Beam Pattern for Night-Time Driver Assistance

Driver assistance is very important in helping the driver in its driving process. Proposed in this paper is a robust method for collision avoidance on the urban road based on the low beam pattern model, which is used to detect objects under night-time condition with an embedded camera. The proposed method consists of two steps. Firstly, the low beam pattern model is computed through perspective transformation and nonlinear regression from the difference signal between the none-beam frame and the beam frame. Secondly, the moving objects are detected by differencing the real-time input video and low beam pattern model. Several night driving videos are adopted in this study and the experimental results demonstrate the feasibility and effectiveness of the proposed method.