Action Recognition from Arbitrary Views Using Transferable Dictionary Learning

Jingtian Zhang, Hubert P. H. Shum, Jungong Han and Ling Shao
IEEE Transactions on Image Processing (TIP), 2018

REF 2021 Submitted Output Impact Factor: 10.6 Top 10% Journal in Computer Science, Artificial Intelligence Citation: 59#

Action Recognition from Arbitrary Views Using Transferable Dictionary Learning
# According to Google Scholar 2023"

Abstract

Human action recognition is crucial to many practical applications, ranging from human-computer interaction to video surveillance. Most approaches either recognize the human action from a fixed view or require the knowledge of view angle, which is usually not available in practical applications. In this paper, we propose a novel end-to-end framework to jointly learn a view-invariance transfer dictionary and a view-invariant classifier. The result of the process is a dictionary that can project real-world 2D video into a view-invariant sparse representation, and a classifier to recognize actions with an arbitrary view. The main feature of our algorithm is the use of synthetic data to extract view-invariance between 3D and 2D videos during the pre-training phase. This guarantees the availability of training data, and removes the hassle of obtaining real-world videos in specific viewing angles. Additionally, for better describing the actions in 3D videos, we introduce a new feature set called the3D dense trajectories to effectively encode extracted trajectory information on 3D videos. Experimental results on the IXMAS, N-UCLA, i3DPost and UWA3DII data sets show improvements over existing algorithms.

Downloads

YouTube

Citations

BibTeX

@article{zhang18arbitrary,
 author={Zhang, Jingtian and Shum, Hubert P. H. and Han, Jungong and Shao, Ling},
 journal={IEEE Transactions on Image Processing},
 title={Action Recognition from Arbitrary Views Using Transferable Dictionary Learning},
 year={2018},
 volume={27},
 number={10},
 pages={4709--4723},
 numpages={15},
 doi={10.1109/TIP.2018.2836323},
 issn={1057-7149},
 publisher={IEEE},
}

RIS

TY  - JOUR
AU  - Zhang, Jingtian
AU  - Shum, Hubert P. H.
AU  - Han, Jungong
AU  - Shao, Ling
T2  - IEEE Transactions on Image Processing
TI  - Action Recognition from Arbitrary Views Using Transferable Dictionary Learning
PY  - 2018
VL  - 27
IS  - 10
SP  - 4709
EP  - 4723
DO  - 10.1109/TIP.2018.2836323
SN  - 1057-7149
PB  - IEEE
ER  - 

Plain Text

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, vol. 27, no. 10, pp. 4709-4723, IEEE, 2018.

Supporting Grants

Northumbria University

Postgraduate Research Scholarship (Ref: ): £65,000, Principal Investigator ()
Received from Faculty of Engineering and Environment, Northumbria University, UK, 2015-2018
Project Page
The Engineering and Physical Sciences Research Council
Interaction-based Human Motion Analysis
EPSRC First Grant Scheme (Ref: EP/M002632/1): £123,819, Principal Investigator ()
Received from The Engineering and Physical Sciences Research Council, UK, 2014-2016
Project Page

Similar Research

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
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
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
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
Meng Li, Howard Leung and Hubert P. H. Shum, "Human Action Recognition via Skeletal and Depth Based Feature Fusion", Proceedings of the 2016 ACM International Conference on Motion in Games (MIG), 2016

 

 

Last updated on 14 April 2024
RSS Feed