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Discriminative poses for early recognition in multi-camera networks

Published: 08 September 2015 Publication History

Abstract

We present a framework for early action recognition in a multi-camera network. Our approach balances recognition accuracy with speed by dynamically selecting the best camera for classification. We follow an iterative clustering approach to learn sets of keyposes that are discriminative for recognition as well as for predicting the best camera for classification of future frames. Experiments on multi-camera datasets demonstrate the applicability of our view-shifting framework to the problem of early recognition.

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Cited By

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  • (2022)Towards Active Vision for Action Localization with Reactive Control and Predictive Learning2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV51458.2022.00345(3391-3400)Online publication date: Jan-2022
  • (2020)Active Vision for Early Recognition of Human Actions2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR42600.2020.00116(1078-1088)Online publication date: Jun-2020

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cover image ACM Other conferences
ICDSC '15: Proceedings of the 9th International Conference on Distributed Smart Cameras
September 2015
225 pages
ISBN:9781450336819
DOI:10.1145/2789116
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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  • Escuela Técnica superier de Ingeniería Informática, Universidad de Seville, Spain: Escuela Técnica superier de Ingeniería Informática, Universidad de Seville, Spain

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2015

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Author Tags

  1. early recognition
  2. exemplar-based learning

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  • Research-article

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ICDSC '15
Sponsor:
  • Escuela Técnica superier de Ingeniería Informática, Universidad de Seville, Spain

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ICDSC '15 Paper Acceptance Rate 43 of 48 submissions, 90%;
Overall Acceptance Rate 92 of 117 submissions, 79%

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Cited By

View all
  • (2022)Towards Active Vision for Action Localization with Reactive Control and Predictive Learning2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV51458.2022.00345(3391-3400)Online publication date: Jan-2022
  • (2020)Active Vision for Early Recognition of Human Actions2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR42600.2020.00116(1078-1088)Online publication date: Jun-2020

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