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2018 | OriginalPaper | Chapter

Exploiting Sub-region Deep Features for Specific Action Recognition in Combat Sports Video

Authors : Yongqiang Kong, Zhaoqiang Wei, Zhengang Wei, Shengke Wang, Feng Gao

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

Current research works for human action recognition in videos mainly focused on the case in different types of videos, that is coarse recognition. However, for recognizing specific actions of one object of interest, these methods may fail to recognize, especially if the video contains multiple moving objects with different actions. In this paper, we proposed a novel method for specific player action recognition in combat sports video. Object tracking with body segmentation are used to generate sub-frame sequences. Action recognition is achieved by training a new three-stream Convolutional Neural Networks (CNNs) model, where the network inputs are horizontal components of optical flow, single sub-frame and vertical components of optical flow, respectively. And the network fusion is applied at both convolutional and softmax layers. Extensive experiments on real broadcast combat sports videos are provided to show the advantages and effectiveness of the proposed method.

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Metadata
Title
Exploiting Sub-region Deep Features for Specific Action Recognition in Combat Sports Video
Authors
Yongqiang Kong
Zhaoqiang Wei
Zhengang Wei
Shengke Wang
Feng Gao
Copyright Year
2018
Book
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77382-7

Electronic ISBN: 978-3-319-77383-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77383-4_19