Vehicle Detection Using Multi-level Probability Fusion Maps Generated by a Multi-camera System

In this paper we describe a multi-camera traffic monitoring system relying on the concept of probability fusion maps (PFM) to detect vehicles in a traffic scene. In the PFM, traffic images from multiple cameras are inverse perspective-mapped and registered onto a common reference frame, combining the multiple camera information to reduce the impact of occlusions. Although the unconstrained perspective projection is non-invertible, imposing the condition that the image points be co-planar allows inversion. However, in a traffic scene, the co-planarity of image points is not strictly true, so the PFM are subject to distortions. We present a new approach that reduces these distortions by projecting the camera images onto planes at different offsets from the road plane. These PFM are combined to generate a multi-level (ML) PFM. We show that the distortions in the various projection planes offset and the ML PFM thus improves vehicle detection in the presence of occlusions.