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Using decision trees to recognize visual events

Published: 31 October 2008 Publication History

Abstract

This paper presents a classifier-based approach to recognize events in video surveillance sequences. The aim of this work is to propose a generic event recognition system that can be used without relying on a long-term tracking procedure. It is composed of three stages. The first one aims at defining and building a set of relevant features from the foreground objects. Second, a clustering tree-based method is used to handle the features and aggregate them locally in a set of coarse to fine activity patterns. Finally, events are modeled as a sequence of structured patterns with an ensemble of randomized trees. In particular, we want this classifier to discover the temporal and causal correlations between the most discriminative patterns. Our system is tested on simulated events and in a real world context with the CAVIAR video sequences dataset. Preliminary results demonstrate the effectiveness of the proposed framework for event recognition in automated visual surveillance applications. We also prove that more flexible algorithms (i.e. deterministic classifiers) rather than probabilistic graph models are conceivable for video events analysis.

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  • (2024)PerMl-Fed: enabling personalized multi-level federated learning within heterogenous IoT environments for activity recognitionCluster Computing10.1007/s10586-024-04289-727:5(6425-6440)Online publication date: 1-Mar-2024

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cover image ACM Conferences
AREA '08: Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
October 2008
132 pages
ISBN:9781605583181
DOI:10.1145/1463542
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 31 October 2008

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Author Tags

  1. activity recognition
  2. automated visual surveillance system
  3. randomized decision trees

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MM08
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MM08: ACM Multimedia Conference 2008
October 31, 2008
British Columbia, Vancouver, Canada

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View all
  • (2024)PerMl-Fed: enabling personalized multi-level federated learning within heterogenous IoT environments for activity recognitionCluster Computing10.1007/s10586-024-04289-727:5(6425-6440)Online publication date: 1-Mar-2024

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