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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Compression-Based Clustering of Video Human Activity Using an ASCII Encoding

verfasst von : Guillermo Sarasa, Aaron Montero, Ana Granados, Francisco B. Rodriguez

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018

Verlag: Springer International Publishing

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Abstract

Human Activity Recognition (HAR) from videos is an important area of computer vision research with several applications. There are a wide number of methods to classify video human activities, not without certain disadvantages such as computational cost, dataset specificity or low resistance to noise, among others. In this paper, we propose the use of the Normalized Compression Distance (NCD), as a complementary approach to identify video-based HAR. We have developed a novel ASCII video data format, as a suitable format to apply the NCD in video. For our experiments, we have used the Activities of Daily Living Dataset, to discriminate several human activities performed by different subjects. The experimental results presented in this paper show that the NCD can be used as an alternative to classical analysis of video HAR.

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Metadaten
Titel
Compression-Based Clustering of Video Human Activity Using an ASCII Encoding
verfasst von
Guillermo Sarasa
Aaron Montero
Ana Granados
Francisco B. Rodriguez
Copyright-Jahr
2018
Buch
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01420-9

Electronic ISBN: 978-3-030-01421-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01421-6_7