TY - JOUR
T1 - Exploiting textures for better action recognition in low-quality videos
AU - Rahman, Saimunur
AU - See, John
AU - Ho, Chiung Ching
N1 - Funding Information:
This work was supported, in part, by the Ministry of Higher Education, Malaysia under Fundamental Research Grant Scheme (FRGS) project FRGS/2/2013/ICT07/MMU/03/4 and Multimedia University, Malaysia.
Funding Information:
The authors thank the Ministry of Higher Education, Malaysia and Multimedia University, Malaysia for supporting this research. They also wish to thank the anonymous reviewers for their comments and suggestions which helped in enhancing the quality of the paper.
Publisher Copyright:
© 2017, The Author(s).
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/11/21
Y1 - 2017/11/21
N2 - Human action recognition is an increasingly matured field of study in the recent years, owing to its widespread use in various applications. A number of related research problems, such as feature representations, human pose and body parts detection, and scene/object context, are being actively studied. However, the general problem of video quality—a realistic issue in the face of low-cost surveillance infrastructure and mobile devices, has not been systematically investigated from various aspects. In this paper, we address the problem of action recognition in low-quality videos from a myriad of perspectives: spatial and temporal downsampling, video compression, and the presence of motion blurring and compression artifacts. To increase the resilience of feature representation in these type of videos, we propose to use textural features to complement classical shape and motion features. Extensive results were carried out on low-quality versions of three publicly available datasets: KTH, UCF-YouTube, HMDB. Experimental results and analysis suggest that leveraging textural features can significantly improve action recognition performance under low video quality conditions.
AB - Human action recognition is an increasingly matured field of study in the recent years, owing to its widespread use in various applications. A number of related research problems, such as feature representations, human pose and body parts detection, and scene/object context, are being actively studied. However, the general problem of video quality—a realistic issue in the face of low-cost surveillance infrastructure and mobile devices, has not been systematically investigated from various aspects. In this paper, we address the problem of action recognition in low-quality videos from a myriad of perspectives: spatial and temporal downsampling, video compression, and the presence of motion blurring and compression artifacts. To increase the resilience of feature representation in these type of videos, we propose to use textural features to complement classical shape and motion features. Extensive results were carried out on low-quality versions of three publicly available datasets: KTH, UCF-YouTube, HMDB. Experimental results and analysis suggest that leveraging textural features can significantly improve action recognition performance under low video quality conditions.
KW - Action recognition
KW - Low-quality video
KW - Motion
KW - Shape
KW - Spatio-temporal features
KW - Textures
UR - http://www.scopus.com/inward/record.url?scp=85035814551&partnerID=8YFLogxK
U2 - 10.1186/s13640-017-0221-2
DO - 10.1186/s13640-017-0221-2
M3 - Article
AN - SCOPUS:85035814551
SN - 1687-5176
VL - 2017
JO - EURASIP Journal on Image and Video Processing
JF - EURASIP Journal on Image and Video Processing
M1 - 74
ER -