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A New Gait-Based Identification Method Using Local Gauss Maps Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Human Action Recognition Using Action Bank Features and Convolutional Neural Networks

Authors : Earnest Paul Ijjina, C. Krishna Mohan

Published in: Computer Vision - ACCV 2014 Workshops

Publisher: Springer International Publishing

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Abstract

With the advancement in technology and availability of multimedia content, human action recognition has become a major area of research in computer vision that contributes to semantic analysis of videos. The representation and matching of spatio-temporal information in videos is a major factor affecting the design and performance of existing convolution neural network approaches for human action recognition. In this paper, in contrast to the traditional approach of using raw video as input, we derive attributes from action bank features to represent and match spatio-temporal information effectively. The derived features are arranged in a square matrix and used as input to the convolutional neural network for action recognition. The effectiveness of the proposed approach is demonstrated on KTH and UCF Sports datasets.

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Metadata
Title
Human Action Recognition Using Action Bank Features and Convolutional Neural Networks
Authors
Earnest Paul Ijjina
C. Krishna Mohan
Copyright Year
2015
Book
Computer Vision - ACCV 2014 Workshops

Print ISBN: 978-3-319-16627-8

Electronic ISBN: 978-3-319-16628-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-16628-5_24

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