3D Pose Estimation of Front Vehicle Towards a Better Driver Assistance System

Abstract

Driver assistance system enhances traffic safety and efficiency. Accurate 3D pose of front vehicle can help driver to make right decisions on road. We propose a novel real-time system to estimate 3D pose of the front vehicle. This system consists of two parallel threads: vehicle rear tracking and mapping. Vehicle rear is firstly identified in the video captured by an on-board camera, after license plate localization and foreground extraction. 3D pose estimation technique is then employed with respect to extracted vehicle rear. Most 3D pose estimation techniques need prior models or a stereo initialization with user cooperation. It is extremely difficult to obtain prior models due to various appearances of vehicle rears. Moreover, it is unsafe to ask for driver's cooperation when vehicle is running. In our system, two initial key frames for stereo algorithm are automatically extracted by vehicle rear detection and tracking. Map points are defined as a collection of point features extracted from vehicle rear with their 3D information. These map points are inferences that relating 2D features detected in following vehicle rears with 3D world. Relative 3D Pose between current vehicle rear and on-board camera is then estimated through mapping that matches map points with current point features. We demonstrate the abilities of our system by augmented reality, which needs accurate and real-time 3D pose estimation.