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Human Action Recognition via Skeletal and Depth Based Feature Fusion

Meng Li, Howard Leung and Hubert P. H. Shum
Proceedings of the 2016 ACM International Conference on Motion in Games (MIG), 2016

 Citation: 35#

Human Action Recognition via Skeletal and Depth Based Feature Fusion
# According to Google Scholar 2023"

Abstract

This paper addresses the problem of recognizing human actions captured with depth cameras. Human action recognition is a challenging task as the articulated action data is high dimensional in both spatial and temporal domains. An effective approach to handle this complexity is to divide human body into different body parts according to human skeletal joint positions, and performs recognition based on these part-based feature descriptors. Since different types of features could share some similar hidden structures, and different actions may be well characterized by properties common to all features (sharable structure) and those specific to a feature (specific structure), we propose a joint group sparse regression-based learning method to model each action. Our method can mine the sharable and specific structures among its part-based multiple features meanwhile imposing the importance of these part-based feature structures by joint group sparse regularization, in favor of discriminative part-based feature structure selection. To represent the dynamics and appearance of the human body parts, we employ part-based multiple features extracted from skeleton and depth data respectively. Then, using the group sparse regularization techniques, we have derived an algorithm for mining the key part-based features in the proposed learning framework. The resulting features derived from the learnt weight matrices are more discriminative for multi-task classification. Through extensive experiments on three public datasets, we demonstrate that our approach outperforms existing methods.

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Citations

BibTeX

@inproceedings{meng16human,
 author={Li, Meng and Leung, Howard and Shum, Hubert P. H.},
 booktitle={Proceedings of the 2016 ACM International Conference on Motion in Games},
 series={MIG '16},
 title={Human Action Recognition via Skeletal and Depth Based Feature Fusion},
 year={2016},
 month={10},
 pages={123--132},
 numpages={10},
 doi={10.1145/2994258.2994268},
 isbn={978-1-4503-4592-7},
 publisher={ACM},
 Address={New York, NY, USA},
 location={San Francisco, USA},
}

RIS

TY  - CONF
AU  - Li, Meng
AU  - Leung, Howard
AU  - Shum, Hubert P. H.
T2  - Proceedings of the 2016 ACM International Conference on Motion in Games
TI  - Human Action Recognition via Skeletal and Depth Based Feature Fusion
PY  - 2016
Y1  - 10 2016
SP  - 123
EP  - 132
DO  - 10.1145/2994258.2994268
SN  - 978-1-4503-4592-7
PB  - ACM
ER  - 

Plain Text

Meng Li, Howard Leung and Hubert P. H. Shum, "Human Action Recognition via Skeletal and Depth Based Feature Fusion," in MIG '16: Proceedings of the 2016 ACM International Conference on Motion in Games, pp. 123-132, San Francisco, USA, ACM, Oct 2016.

Supporting Grants

Similar Research

Qianhui Men, Edmond S. L. Ho, Hubert P. H. Shum and Howard Leung, "Focalized Contrastive View-Invariant Learning for Self-Supervised Skeleton-Based Action Recognition", Neurocomputing, 2023
Edmond S. L. Ho, Jacky C. P. Chan, Donald C. K. Chan, Hubert P. H. Shum, Yiu-ming Cheung and P. C. Yuen, "Improving Posture Classification Accuracy for Depth Sensor-Based Human Activity Monitoring in Smart Environments", Computer Vision and Image Understanding (CVIU), 2016
Zheming Zuo, Daniel Organisciak, Hubert P. H. Shum and Longzhi Yang, "Saliency-Informed Spatio-Temporal Vector of Locally Aggregated Descriptors and Fisher Vectors for Visual Action Recognition", Proceedings of the 2018 British Machine Vision Conference Workshop on Image Analysis for Human Facial and Activity Recognition (IAHFAR), 2018
Zhengzhi Lu, He Wang, Ziyi Chang, Guoan Yang and Hubert P. H. Shum, "Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient", Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023
Jingtian Zhang, Lining Zhang, Hubert P. H. Shum and Ling Shao, "Arbitrary View Action Recognition via Transfer Dictionary Learning on Synthetic Training Data", Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016
Jingtian Zhang, Hubert P. H. Shum, Jungong Han and Ling Shao, "Action Recognition from Arbitrary Views Using Transferable Dictionary Learning", IEEE Transactions on Image Processing (TIP), 2018

 

 

Last updated on 17 February 2024
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