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Recognizing Human Actions by Using Effective Codebooks and Tracking

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Advanced Topics in Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

Recognition and classification of human actions for annotation of unconstrained video sequences has proven to be challenging because of the variations in the environment, appearance of actors, modalities in which the same action is performed by different persons, speed and duration and points of view from which the event is observed. This variability reflects in the difficulty of defining effective descriptors and deriving appropriate and effective codebooks for action categorization. In this chapter, we present a novel and effective solution to classify human actions in unconstrained videos. In the formation of the codebook, we employ radius-based clustering with soft assignment in order to create a rich vocabulary that may account for the high variability of human actions. We show that our solution scores very good performance with no need of parameter tuning. We also show that a strong reduction of computation time can be obtained by applying codebook size reduction with Deep Belief Networks with little loss of accuracy.

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Notes

  1. 1.

    Please note that an earlier version of this work has recently appeared in IEEE Transactions on Multimedia [4].

  2. 2.

    http://lastlaugh.inf.cs.cmu.edu/libscom/downloads.htm

  3. 3.

    http://vision.ucsd.edu/%7epdollar/research.html

  4. 4.

    http://www.irisa.fr/vista/Equipe/People/Laptev/download.html

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Authors and Affiliations

  1. Media Integration and Communication Center, University of Florence, Viale Morgagni 65, 50134, Florence, Italy

    Lamberto Ballan, Lorenzo Seidenari, Giuseppe Serra, Marco Bertini & Alberto Del Bimbo

Authors
  1. Lamberto Ballan
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  2. Lorenzo Seidenari
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  3. Giuseppe Serra
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  4. Marco Bertini
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  5. Alberto Del Bimbo
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Corresponding author

Correspondence to Lamberto Ballan .

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Editors and Affiliations

  1. Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy

    Giovanni Maria Farinella

  2. Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy

    Sebastiano Battiato

  3. Department of Engineering, University of Cambridge, Cambridge, United Kingdom

    Roberto Cipolla

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Ballan, L., Seidenari, L., Serra, G., Bertini, M., Del Bimbo, A. (2013). Recognizing Human Actions by Using Effective Codebooks and Tracking. In: Farinella, G., Battiato, S., Cipolla, R. (eds) Advanced Topics in Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5520-1_3

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  • DOI: https://doi.org/10.1007/978-1-4471-5520-1_3

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5519-5

  • Online ISBN: 978-1-4471-5520-1

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