Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization
Abstract
This article falls under the problem of the symbolic monitoring of real-time complex systems or of video interpretation systems. Among the various techniques used for the on-line monitoring, we are interested here in the temporal scenario recognition. In order to reduce the complexity of the recognition and, consequently, to improve its performance, we explore two methods: the first one is the focus on particular events (in practice, uncommon ones) and the second one is the factorization of common temporal scenarios in order to do a hierarchical recognition. In this article, we present both concepts and merge them to propose a focused hierarchical recognition. This approach merges and generalizes the two main approaches in symbolic recognition of temporal scenarios: the Store Totally Recognized Scenarios (STRS) approach and the Store Partially Recognized Scenarios (SPRS) approach. URL: http://crs.elibel.tm.fr/xophe/pdf/2007-ijcai.pdf
Cite
Text
Dousson and Le Maigat. "Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Dousson and Le Maigat. "Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/dousson2007ijcai-chronicle/)BibTeX
@inproceedings{dousson2007ijcai-chronicle,
title = {{Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization}},
author = {Dousson, Christophe and Le Maigat, Pierre},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {324-329},
url = {https://mlanthology.org/ijcai/2007/dousson2007ijcai-chronicle/}
}