Citation

See, John Su Yang and Rahman, Saimunur (2016) On the Effects of Low Video Quality in Human Action Recognition. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, pp. 1-8. ISBN 978-1-4673-6795-0

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Abstract

Human activity recognition is one of the most intensively studied areas of computer vision and pattern recognition in recent years. A wide variety of approaches have shown to work well against challenging image variations such as appearance, pose and illumination. However, the problem of low video quality remains an unexplored and challenging issue in real-world applications. In this paper, we investigate the effects of low video quality in human action recognition from two perspectives: videos that are poorly sampled spatially (low resolution) and temporally (low frame rate), and compressed videos affected by motion blurring and artifacts. In order to increase the robustness of feature representation under these conditions, we propose the usage of textural features to complement the popular shape and motion features. Extensive experiments were carried out on two well-known benchmark datasets of contrasting nature: the classic KTH dataset and the large-scale HMDB51 dataset. Results obtained with two popular representation schemes (Bag-of-Words, Fisher Vectors) further validate the effectiveness of the proposed approach.

Item Type:	Book Section
Uncontrolled Keywords:	Streaming media, Feature extraction, Shape, Histograms, Spatial resolution, Pattern recognition, Video recording
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	29 Jan 2018 17:12
Last Modified:	29 Jan 2018 17:12
URII:	http://shdl.mmu.edu.my/id/eprint/6660

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