Robust image processing for an omnidirectional camera-based smart car door

Abstract

Over the last one decade there has been an increasing emphasis on driver-assistance systems for the automotive domain. In this paper we report our work on designing a camera-based surveillance system embedded in a “smart” car door. Such a camera is used to monitor the ambient environment outside the car — e.g., the presence of obstacles such as approaching cars or cyclists who might collide with the car door if opened — and automatically control the car door operations. This is an enhancement to the currently available side-view mirrors which the driver/passenger checks before opening the car door. The focus of this paper is on fast and robust image processing algorithms specifically targeting such a smart car door system. The requirement is to quickly detect traffic objects of interest from gray-scale images captured by omnidirectional cameras. Whereas known algorithms for object extraction from the image processing literature rely on color information and are sensitive to shadows and illumination changes, our proposed algorithms are highly robust, can operate on gray-scale images (color images are not available in our setup) and output results in real-time. To illustrate these, we present a number of experimental results based on image sequences captured from real-life traffic scenarios.