---
title: "Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization"
authors:
  - "Christophe Dousson"
  - "Pierre Le Maigat"
doi: ""
url: "https://ijcai.org/proceedings/2007/50"
pdf: "https://ijcai.org/proceedings/2007/0050.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "324-328"
track: ""
keywords:
---

# Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization

Christophe Dousson, Pierre Le Maigat

*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.