Rule-based reasoning on visual data for urban traffic monitoring

The paper describes a system for detecting vehicles in urban traffic scenes by means of rule-based reasoning on visual data. The strength of the proposed approach is its formal separation between low-level image processing modules (able for extracting visual data under various illumination conditions) and the high-level module, which provides a single framework for tracking vehicles in the scene. The image processing modules extract visual data from the scene, by spatio-temporal analysis during day-time, and by morphological analysis of headlights at night. The high-level module is designed as a forward chaining production rule system, working on symbolic data, i.e. vehicles and their attributes (area, pattern, direction...) and exploiting a set of heuristic rules tuned to urban traffic conditions. The synergy between the artificial intelligence techniques of the high level and the low-level image analysis techniques provides the system with flexibility and robustness.