Robust Feature-Based Automated Multi-View Human Action Recognition System

IEEE Access

Michael Blumenstein¹  Di Wu¹  Nabin Sharma¹  Chin-Teng Lin¹  Mukesh Prasad¹  Kuang-Pen Chou²  Wen-Chieh Lin²  Yu-Feng Lin²  Dong-Lin Li³ 

[1] Centre for Artficial Intelligence, School of Software, FEIT, University of Technology Sydney, Sydney, NSW, Australia;Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan;Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan;

关键词: Multi-view video;    action recognition;    feature extraction;    background subtraction;    classification;    machine learning;   

DOI  :  10.1109/ACCESS.2018.2809552

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown