PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Spatio-temporal reasoning for traffic scene understanding
Raluca Brehar, Carolina Fortuna, Silviu Bota, Dunja Mladenić and Sergiu Nedevschi
In: ICCP 2011, 25-27 Aug 2011, Cluj-Napoca, Romania.

Abstract

In this paper we introduce a system for semantic understanding of traffic scenes. The system detects objects in video images captured in real vehicular traffic situations, classifies them, maps them to the OpenCyc1 ontology and finally generates descriptions of the traffic scene in CycL or cvasi-natural language. We employ meta-classification methods based on AdaBoost and Random forest algorithms for identifying interest objects like: cars, pedestrians, poles in traffic and we derive a set of annotations for each traffic scene. These annotations are mapped to OpenCyc concepts and predicates, spatiotemporal rules for object classification and scene understanding are then asserted in the knowledge base. Finally, we show that the system performs well in understanding traffic scene situations and summarizing them. The novelty of the approach resides in the combination of stereo-based object detection and recognition methods with logic based spatio-temporal reasoning.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Information Retrieval & Textual Information Access
ID Code:8719
Deposited By:Jan Rupnik
Deposited On:21 February 2012