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Erschienen in:
An Analysis of Reordering Algorithms to Reduce the Computational Cost of the Jacobi-Preconditioned CG Solver Using High-Precision Arithmetic Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

A Comprehensive Survey on Human Activity Prediction

verfasst von : Nghia Pham Trong, Hung Nguyen, Kotani Kazunori, Bac Le Hoai

Erschienen in: Computational Science and Its Applications – ICCSA 2017

Verlag: Springer International Publishing

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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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Vorheriges Kapitel An Implementation of Parallel 1-D Real FFT on Intel Xeon Phi Processors
Nächstes Kapitel Using Ontology for Personalised Course Recommendation Applications
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Metadaten
Titel
A Comprehensive Survey on Human Activity Prediction
verfasst von
Nghia Pham Trong
Hung Nguyen
Kotani Kazunori
Bac Le Hoai
Copyright-Jahr
2017
Buch
Computational Science and Its Applications – ICCSA 2017

Print ISBN: 978-3-319-62391-7

Electronic ISBN: 978-3-319-62392-4

Copyright-Jahr: 2017

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