Vision-based potential collision detection for reversing vehicle
This paper deals with the detection of potential collision for a reversing vehicle. The collision detection relies on monitoring the surroundings of reversing vehicle by means of on-board cameras mounted on the side window and rear side of the vehicle. We first detect the stationary vehicle(s) in the region of interest (ROI) using histogram of oriented gradients (HOG) features and support vector machine (SVM) based classification. This is followed by the use of a Gaussian Mixture Model (GMM) to detect dynamic vehicle(s) in the ROI. Trajectories and speeds of the dynamic vehicles are predicted through the particular position of the detected vehicles in each frame and through their observed trajectory and speed patterns. Finally a potential collision is predicted based on the trajectories and speeds of both vehicles. The experimental results indicate that the proposed framework performs well in different traffic scenarios and can contribute to the advancement for improving the safety of car driving.