Image Analysis and Rule-Based Reasoning for a Traffic Monitoring System

被引：232

作者：

Cucchiara, Rita ^{[1
,4
,5
]}

Piccardi, Massimo ^{[2
,4
,6
]}

Mello, Paola ^{[3
,4
]}

机构：

[1] Dipto. di Scienze dell'Ingegneria, Univ. di Modena e Reggio Emilia, 41100 Modena, Italy

[2] Dipartimento di Ingegneria, Università di Ferrara, 44100 Ferrara, Italy

[3] DEIS Università di Bologna, 40126 Bologna, Italy

[4] University of Bologna, Bologna, Italy

[5] Department of Computer Science, Faculty of Engineering, Univ. of Modena and Reggio Emilia, Modena, Italy

[6] Department of Computer Science, Faculty of Engineering, University of Ferrara, Ferrara, Italy

来源：

IEEE Transactions on Intelligent Transportation Systems | 2000年 / 1卷 / 02期

关键词：

D O I：

10.1109/6979.880969

中图分类号：

学科分类号：

摘要：

The paper presents an approach for detecting vehicles in urban traffic scenes by means of rule-based reasoning on visual data. The strength of the approach is its formal separation between the low-level image processing modules (used for extracting visual data under various illumination conditions) and the high-level module, which provides a general-purpose knowledge-based framework for tracking vehicles in the scene. The image-processing modules extract visual data from the scene by spatio-temporal analysis during daytime, 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 others) 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.

引用

页码：119 / 130