A multimedia system architecture for automatic annotation of sports videos

Authors:
William Christmas;Edward Jaser;Kieron Messer;Josef Kittler
Affiliations:
University of Surrey, UK;University of Surrey, UK;University of Surrey, UK;University of Surrey, UK
Venue:
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Year:
2003

Citing 4
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
A framework for recognizing multi-agent action from visual evidence

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Induction of Decision Trees

Machine Learning
On growing better decision trees from data

On growing better decision trees from data

Quantified Score

Hi-index	0.00

Visualization

Abstract

ASSAVID is an EU-sponsored project which is concerned with the development of a system for the automatic segmentation and semantic annotation of sports video material. In this paper we describe the architecture for a system that automatically creates high-level textual annotation for this material, to create a fully automatic sports video logging process. The proposed technique relies upon the concept of "cues" which attach semantic meaning to low-level features computed on the video and audio. Experimentalresul ts on sports video provided by the BBC demonstrate that this method is working well. The system merges and synchronises severalstreams of cues derived from the video and audio sources, where each stream may have a different latency.