3D Action Recognition Using Multi-Temporal Skeleton Visualization

Keywords

3D action recognition; convolutional neural networks; skeleton data; visualization

Abstract

Action recognition using depth sequences plays important role in many fields, e.g., intelligent surveillance, content-based video retrieval. Real applications require robust and accurate action recognition method. In this paper, we propose a skeleton visualization method, which efficiently encodes the spatial-temporal information of skeleton joints into a set of color images. These images are served as inputs for convolutional neural networks to extract more discriminative deep features. To enhance the ability of deep features to capture global relationships, we extend the color images into multi-temporal version. Additionally, to solve the effect of view point changes, a spatial transform method is adopted as a preprocessing step. Extensive experiments on NTU RGB+D dataset and ICME2017 challenge show that our method can accurately distinguish similar actions and shows robustness to view variations.

Publication Date

9-5-2017

Publication Title

2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017

Number of Pages

623-626

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICMEW.2017.8026280

Socpus ID

85031705320 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85031705320

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