Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 324-328.
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.
@inproceedings{ijcai2007p50,
title = {Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization},
author = {Dousson, Christophe and Le Maigat, Pierre},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {324--328},
year = {2007},
url = {https://ijcai.org/proceedings/2007/50},
}
TY - CPAPER TI - Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization AU - Dousson, Christophe AU - Le Maigat, Pierre T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 324 EP - 328 UR - https://ijcai.org/proceedings/2007/50 ER -Copied
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