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2018 | OriginalPaper | Chapter

An Efficient Framework Based on Segmented Block Analysis for Human Activity Recognition

Authors : Vikas Tripathi, Durgaprasad Gangodkar, Monika Pandey, Vishal Sanserwal

Published in: Information and Decision Sciences

Publisher: Springer Singapore

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Abstract

Video surveillance systems core component is human activity recognition. Analysis and identification of human activity emphasize on understanding human behavior in the video. Human activity recognition aims to automatically conjecture the activity being acted by a person. In this paper, we propose a novel feature description algorithm in which a segmented block of logarithm-based motion-generating frames is normalized for analysis of action being performed in the image sequences. The features obtained are classified using random forest classifier. We evaluated the framework on HMDB-51 and ATM datasets and achieved an average accuracy of 58.24 and 93.57%.

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Metadata
Title
An Efficient Framework Based on Segmented Block Analysis for Human Activity Recognition
Authors
Vikas Tripathi
Durgaprasad Gangodkar
Monika Pandey
Vishal Sanserwal
Copyright Year
2018
Publisher
Springer Singapore
Book
Information and Decision Sciences

Print ISBN: 978-981-10-7562-9

Electronic ISBN: 978-981-10-7563-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7563-6_42

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