3D Action Recognition Using Data Visualization And Convolutional Neural Networks

Keywords

3D action recognition; Convolutional neural networks; Data visualization; Skeleton data

Abstract

It remains a challenge to efficiently represent spatial-temporal data for 3D action recognition. To solve this problem, this paper presents a new skeleton-based action representation using data visualization and convolutional neural networks, which contains four main stages. First, skeletons from an action sequence are mapped as a set of five dimensional points, containing three dimensions of location, one dimension of time label and one dimension of joint label. Second, these points are encoded as a series of color images, by visualizing points as RGB pixels. Third, convolutional neural networks are adopted to extract deep features from color images. Finally, action class score is calculated by fusing selected deep features. Extensive experiments on three benchmark datasets show that our method achieves state-of-the-art results.

Publication Date

8-28-2017

Publication Title

Proceedings - IEEE International Conference on Multimedia and Expo

Number of Pages

925-930

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICME.2017.8019438

Socpus ID

85030225316 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85030225316

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