Robust feature-based automated multi-view human action recognition system

Article

Chou, Kuang-Pen, Prasad, Mukesh, Wu, Di, Sharma, Nabin, Li, Dong-Lin, Lin, Yu-Feng, Blumenstein, Michael, Lin, Wen-Chieh and Lin, Chin-Teng. 2018. "Robust feature-based automated multi-view human action recognition system." IEEE Access. 6, pp. 15283-15296. https://doi.org/10.1109/ACCESS.2018.2809552
Article Title

Robust feature-based automated multi-view human action recognition system

ERA Journal ID210567
Article CategoryArticle
AuthorsChou, Kuang-Pen, Prasad, Mukesh, Wu, Di, Sharma, Nabin, Li, Dong-Lin, Lin, Yu-Feng, Blumenstein, Michael, Lin, Wen-Chieh and Lin, Chin-Teng
Journal TitleIEEE Access
Journal Citation6, pp. 15283-15296
Number of Pages14
Year2018
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN2169-3536
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2018.2809552
Web Address (URL)https://ieeexplore.ieee.org/abstract/document/8302448
Abstract

Automated human action recognition has the potential to play an important role in public security, for example, in relation to the multiview surveillance videos taken in public places, such as train stations or airports. This paper compares three practical, reliable, and generic systems for multiview video-based human action recognition, namely, the nearest neighbor classifier, Gaussian mixture model classifier, and the nearest mean classifier. To describe the different actions performed in different views, view-invariant features are proposed to address multiview action recognition. These features are obtained by extracting the holistic features from different temporal scales which are modeled as points of interest which represent the global spatial-temporal distribution. Experiments and cross-data testing are conducted on the KTH, WEIZMANN, and MuHAVi datasets. The system does not need to be retrained when scenarios are changed which means the trained database can be applied in a wide variety of environments, such as view angle or background changes. The experiment results show that the proposed approach outperforms the existing methods on the KTH and WEIZMANN datasets.

KeywordsMulti-view video; action recognition; feature extraction; background subtraction; classification; machine learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
460304. Computer vision
Byline AffiliationsNational Chiao Tung University, Taiwan
University of Technology Sydney
Permalink -

https://research.usq.edu.au/item/z4y25/robust-feature-based-automated-multi-view-human-action-recognition-system

Log in to edit

Download files

Published Version
Robust_Feature-Based_Automated_Multi-View_Human_Action_Recognition_System.pdf
License: CC BY 4.0
File access level: Anyone

  • 4
    total views
  • 3
    total downloads
  • 4
    views this month
  • 3
    downloads this month

Export as

Related outputs

Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey
Rao, Bosen, Zhang, Jiale, Wu, Di, Zhu, Chengcheng, Sun, Xiaobing and Chen, Bing. 2024. "Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey." IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/TAI.2024.3363670
VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems
Zhang, Jiale, Liu, Yue, Wu, Di, Lou, Shuai, Chen, Bing and Yu, Shui. 2023. "VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems." Digital Communications and Networks. 9 (4), pp. 981-989. https://doi.org/10.1016/j.dcan.2022.05.010
Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things
Wang, Nai, Chen, Junjun, Wu, Di, Yang, Wencheng, Xiang, Yong and Sajjanhar, Atul. 2023. "Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things." Journal of Information Security and Applications. 75. https://doi.org/10.1016/j.jisa.2023.103483
From distributed machine learning to federated learning: In the view of data privacy and security
Shen, Sheng, Zhu, Tianqing, Wu, Di, Wang, Wei and Zhou, Wanlei. 2022. "From distributed machine learning to federated learning: In the view of data privacy and security." Concurrency and Computation: Practice and Experience. 34 (16). https://doi.org/10.1002/cpe.6002
A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning
Wu, Di, Wang, Nai, Zhang, Jiale, Zhang, Yuan, Xiang, Yong and Gao, Longxiang. 2022. "A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning." 2022 International Joint Conference on Neural Networks (IJCNN). Padua, Italy 18 - 23 Jul 2022 IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN55064.2022.9892039
Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks
Zhao, Ying, Chen, Junjun, Zhang, Jiale, Wu, Di, Blumenstein, Michael and Yu, Shui. 2022. "Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks." Concurrency and Computation: Practice and Experience. 34 (7). https://doi.org/10.1002/cpe.5906
An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition
Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2019. "An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition." 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). Auckland, New Zealand 19 - 21 Nov 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IVCNZ.2018.8634660
Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos
Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2019. "Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos." 2018 Digital Image Computing: Techniques and Applications (DICTA). Canberra, Australia 10 - 13 Dec 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/DICTA.2018.8615804
Adversarial action data augmentation for similar gesture action recognition
Wu, Di, Chen, Junjun, Sharma, Nabin, Pan, Shirui, Long, Guodong and Blumenstein, Michael. 2019. "Adversarial action data augmentation for similar gesture action recognition." 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary 14 - 19 Jul 2019 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2019.8851993
Feature-dependent graph convolutional autoencoders with adversarial training methods
Wu, Di, Hu, Ruiqi, Zheng, Yu, Jiang, Jing, Sharma, Nabin and Blumenstein, Michael. 2019. "Feature-dependent graph convolutional autoencoders with adversarial training methods." 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary 14 - 19 Jul 2019 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2019.8852314
Poisoning attack in federated learning using generative adversarial nets
Zhang, Jiale, Chen, Junjun, Wu, Di, Chen, Bing and Yu, Shui. 2019. "Poisoning attack in federated learning using generative adversarial nets." 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). Rotorua, New Zealand 05 - 08 Aug 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/TrustCom/BigDataSE.2019.00057
Network anomaly detection by using a time-decay closed frequent pattern
Zhao, Ying, Chen, Junjun, Wu, Di, Teng, Jian, Sharma, Nabin, Sajjanhar, Atul and Blumenstein, Michael. 2019. "Network anomaly detection by using a time-decay closed frequent pattern." Information (Basel). 10 (8). https://doi.org/10.3390/info10080262
Multi-task network anomaly detection using federated learning
Zhao, Ying, Chen, Junjun, Wu, Di, Teng, Jian and Yu, Shui. 2019. "Multi-task network anomaly detection using federated learning." 10th international symposium on information and communication technology (SoICT 2019). Hanoi, Viet Nam 04 - 06 Dec 2019 United States. Association for Computing Machinery (ACM). https://doi.org/10.1145/3368926.3369705
Recent advances in video-based human action recognition using deep learning: A review
Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2017. "Recent advances in video-based human action recognition using deep learning: A review." 2017 International Joint Conference on Neural Networks (IJCNN). Anchorage, United States 14 - 19 May 2017 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2017.7966210
On addressing the imbalance problem: a correlated KNN approach for network traffic classification
Wu, Di, Chen, Xiao, Chen, Chao, Zhang, Jun, Xiang, Yang and Zhou, Wanlei. 2015. "On addressing the imbalance problem: a correlated KNN approach for network traffic classification." NSS 2014: 8th International Conference on Network and System Security. Xi'an, China 15 - 17 Oct 2014 Switzerland . Springer. https://doi.org/10.1007/978-3-319-11698-3_11
Detecting stepping stones by abnormal causality probability
Wen, Sheng, Wu, Di, Li, Ping, Xiang, Yang, Zhou, Wanlei and Wei, Guiyi. 2015. "Detecting stepping stones by abnormal causality probability." Security and Communication Networks. 8 (10), pp. 1831-1844. https://doi.org/10.1002/sec.1037
Harnessing mutuality: the social capital of club experience - a framework for examining social dynamics of community clubs
Prasad, Mukesh. 2012. Harnessing mutuality: the social capital of club experience - a framework for examining social dynamics of community clubs. PhD Thesis Doctor of Philosophy. University of Southern Queensland.
A Survey on Latest Botnet Attack and Defense
Zhang, Lei, Yu, Shui, Wu, Di and Watters, Paul. 2011. "A Survey on Latest Botnet Attack and Defense ." 10th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2011). Changsha, China 16 - 18 Nov 2011 China. https://doi.org/10.1109/TrustCom.2011.11