Parametric Ego-Motion Estimation for Vehicle Surround Analysis Using Omni-Directional Camera

Abstract

Omni-directional cameras which give 360 degree panoramic view of the surroundings have recently been used in many applications such as robotics, navigation and surveillance. This paper describes the application of parametric ego-motion estimation for vehicle detection to perform surround analysis using an automobile mounted camera. For this purpose, the parametric planar motion model is integrated with the transformations to compensate distortion in omni-directional images. The framework is used to detect objects with independent motion or height above the road. Camera calibration as well as the approximate vehicle speed obtained from CAN bus are integrated with the motion information from spatial and temporal gradients using Bayesian approach. The approach is tested for various configurations of automobile mounted omni camera as well as rectilinear camera. Successful detection and tracking of moving vehicles, and generation of surround map is demonstrated for application to intelligent driver support. Key words Motion estimation, Panoramic vision, Intelligent vehicles, Driver support systems, Collision avoidance