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Hierarchical topic modeling with Pose-transition feature for action recognition using 3D skeleton data
Citation
Huynh-The, T and Hua, C-H and Tu, NA and Hur, T and Bang, J and Kim, D and Amin, MB and Kang, BH and Seung, H and Shin, S-Y and Kim, E-S and Lee, S, Hierarchical topic modeling with Pose-transition feature for action recognition using 3D skeleton data, Information Sciences, 444 pp. 20-35. ISSN 0020-0255 (2018) [Professional, Refereed Article]
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DOI: doi:10.1016/j.ins.2018.02.042
Abstract
Despite impressive achievements in image processing and artificial intelligence in the past decade, understanding video-based action remains a challenge. However, the intensive development of 3D computer vision in recent years has brought more potential research opportunities in pose-based action detection and recognition. Thanks to the advantages of depth camera devices like the Microsoft Kinect sensor, we developed an effective approach to in-depth analysis of indoor actions using skeleton information, in which skeleton-based feature extraction and topic model-based learning are two major contributions. Geometric features, i.e. joint distance, joint angle, and joint-plane distance are calculated in the spatio-temporal dimension. These features are merged into two types, called pose and transition features, and then are provided to codebook construction to convert sparse features into visual words by k-means clustering. An efficient hierarchical model is developed to describe the full correlation of feature - poselet - action based on Pachinko Allocation Model. This model has the potential to uncover more hidden poselets, which have been recognized as the valuable information and help to differentiate pose-sharing actions. The experimental results on several well-known datasets, such as MSR Action 3D, MSR Daily Activity 3D, Florence 3D Action, UTKinect-Action 3D, and NTU RGB+D Action Recognition, demonstrate the high recognition accuracy of the proposed method. Our method outperforms state-of-the-art methods in the field in most dataset benchmarks.
Item Details
| Item Type: | Professional, Refereed Article |
|---|---|
| Keywords: | 3D action recognition, topic modeling, pose-transition feature, Pachinko Allocation Model, depth camera. |
| Research Division: | Information and Computing Sciences |
| Research Group: | Data management and data science |
| Research Field: | Information retrieval and web search |
| Objective Division: | Information and Communication Services |
| Objective Group: | Information systems, technologies and services |
| Objective Field: | Information systems, technologies and services not elsewhere classified |
| UTAS Author: | Amin, MB (Dr Muhammad Bilal Amin) |
| UTAS Author: | Kang, BH (Professor Byeong Kang) |
| ID Code: | 124914 |
| Year Published: | 2018 |
| Web of Science® Times Cited: | 26 |
| Deposited By: | Information and Communication Technology |
| Deposited On: | 2018-03-19 |
| Last Modified: | 2021-03-23 |
| Downloads: | 2 View Download Statistics |
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