Paper Info
Title
Robust Feature-Based Automated Multi-View Human Action Recognition System.
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.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2809552
IEEE ACCESS
Keywords
Field
DocType
Multi-view video,action recognition,feature extraction,background subtraction,classification,machine learning
Background subtraction,Pattern recognition,Computer science,Action recognition,Robustness (computer science),Feature extraction,Artificial intelligence,Feature based,Point of interest,Classifier (linguistics),Public security,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
2
PageRank 
References 
Authors
0.36
0
9
Name
Order
Citations
PageRank
Kuang-pen Chou173.13
Mukesh Prasad216626.33
Di Wu3636117.73
Nabin Sharma413211.55
Dong-Lin Li5224.71
yufeng lin620.69
Michael Blumenstein7474.20
Wen-Chieh Lin847644.50
Chin-Teng Lin93840392.55