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Towards a Cloud Robotics Platform for Distributed Visual SLAM Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Recognition of Human Continuous Action with 3D CNN

Authors : Gang Yu, Ting Li

Published in: Computer Vision Systems

Publisher: Springer International Publishing

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Abstract

Under the boom of the service robot, the human continuous action recognition becomes an indispensable research. In this paper, we propose a continuous action recognition method based on multi-channel 3D CNN for extracting multiple features, which are classified with KNN. First, we use fragmentary action as training samples which can be identified in the process of action. Then the training samples are processed through the gray scale, improved L-K optical flow and Gabor filter, to extract the characteristics of diversification using a priori knowledge. Then the 3D CNN is constructed to process multi-channel features that are formed into 128-dimension feature maps. Finally, we use KNN to classify those samples. We find that the fragmentary action in continuous action of the identification showed a good robustness. And the proposed method is verified in HMDB-51 and UCF-101 to be more accurate than Gaussian Bayes or the single 3D CNN in action recognition.

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Metadata
Title
Recognition of Human Continuous Action with 3D CNN
Authors
Gang Yu
Ting Li
Copyright Year
2017
Book
Computer Vision Systems

Print ISBN: 978-3-319-68344-7

Electronic ISBN: 978-3-319-68345-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68345-4_28

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