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A Comprehensive Survey on Human Activity Prediction

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Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10404))

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Abstract

Human activity recognition has been extensively studied and achieves promising results in Computer Vision community. Typical activity recognition methods require observe the whole process, then extract features and build a model to classify the activity. However, in many applications, the ability to early recognition or prediction a human activity before it completes is necessary. This task is challenging because of the lack of information when only a fraction of the activity is observed. To get an accurate prediction, the methods must have high discriminated power with just the beginning part of activity. While activity recognition is very popular and has a lot of surveys, activity prediction is still a new and relatively unexplored problem. To the best of our knowledge, there is no survey specifically focusing on human activity prediction. In this survey, we give a systematic review of current methods for activity prediction and how they overcome the above challenge. Moreover, this paper also compares performances of various techniques on the common dataset to show the current state of research.

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Acknowledgments

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED).

Author information

Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Nghia Pham Trong, Hung Nguyen & Kotani Kazunori

  2. University of Science, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Bac Le Hoai

Authors
  1. Nghia Pham Trong
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  2. Hung Nguyen
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  3. Kotani Kazunori
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  4. Bac Le Hoai
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Corresponding author

Correspondence to Nghia Pham Trong .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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Trong, N.P., Nguyen, H., Kazunori, K., Le Hoai, B. (2017). A Comprehensive Survey on Human Activity Prediction. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_30

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  • DOI: https://doi.org/10.1007/978-3-319-62392-4_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62391-7

  • Online ISBN: 978-3-319-62392-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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