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2017 | OriginalPaper | Chapter

Leveraging Textural Features for Recognizing Actions in Low Quality Videos

Authors : Saimunur Rahman, John See, Chiung Ching Ho

Published in: 9th International Conference on Robotic, Vision, Signal Processing and Power Applications

Publisher: Springer Singapore

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Abstract

Human action recognition is a well researched problem, which is considerably more challenging when video quality is poor. In this paper, we investigate human action recognition in low quality videos by leveraging the robustness of textural features to better characterize actions, instead of relying on shape and motion features may fail under noisy conditions. To accommodate videos, texture descriptors are extended to three orthogonal planes (TOP) to extract spatio-temporal features. Extensive experiments were conducted on low quality versions of the KTH and HMDB51 datasets to evaluate the performance of our proposed approaches against standard baselines. Experimental results and further analysis demonstrated the usefulness of textural features in improving the capability of recognizing human actions from low quality videos.

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Literature
1.
Schüldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In: Proceedings of international conference on pattern recognition, pp 32–36
2.
Laptev I, Marszałek M, Schmid C (2008) Rozenfeld B Learning realistic human actions from movies. In: IEEE CVPR, pp 1–8
3.
Kläser A, Marszałek M, Schmid C (2008) A spatio-temporal descriptor based on 3d-gradients. In: BMVC, pp 275–1
4.
Kellokumpu V, Zhao G, Pietikäinen M (2008) Human activity recognition using a dynamic texture based method. In: BMVC, vol 1, pp 88.1–88.10
5.
Mattivi R, Shao L (2009) Human action recognition using LBP-TOP as sparse spatio-temporal feature descriptor. In: Proceedings of CAIP, pp 740–747
6.
Wang, H., Ullah, M.M., Klaser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: BMVC. (2009) 124–1
7.
Wang H, Kläser A, Schmid C, Liu CL (2011) Action recognition by dense trajectories. In: IEEE CVPR, pp 3169–3176
8.
Kuehne H, Jhuang H, Garrote E, Poggio T, Serre T (2011) HMDB: a large video database for human motion recognition. In: IEEE ICCV, pp 2556–2563
9.
Rahman S, See J, Ho CC (2015) Action recognition in low quality videos by jointly using shape, motion and texture features. In: IEEE international conference on signal and image processing, pp 83–88
10.
See J, Rahman S (2015) On the effects of low video quality in human action recognition. In: Proceedings of digital image computing: techniques and applications (DICTA) (to appear)
11.
Laptev I (2005) On space-time interest points. Int J Comput Vis 64(2–3):107–123CrossRef
12.
Wang H, Schmid C (2013) Action recognition with improved trajectories. In: ICCV, IEEE, pp 3551–3558
13.
Zhao G, Pietikainen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE PAMI 29(6):915–928CrossRef
14.
Oh S, Hoogs A, Perera A et al (2011) A large-scale benchmark dataset for event recognition in surveillance video. In: IEEE CVPR, pp 3153–3160
15.
Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans PAMI 28(12):2037–2041CrossRefMATH
16.
Ojansivu V, Heikkilä J (2008) Blur insensitive texture classification using local phase quantization. In: Image and signal processing. Springer, pp 236–243
17.
Kannala J, Rahtu E (2012) Bsif: Binarized statistical image features. In: 2012 21st International Conference on Pattern recognition (ICPR), pp 1363–1366
18.
Päivärinta J, Rahtu E, Heikkilä J (2011) Volume local phase quantization for blur-insensitive dynamic texture classification. In: Image analysis, pp 360–369
19.
Vedaldi A, Zisserman A (2012) Efficient additive kernels via explicit feature maps. IEEE PAMI 34(3):480–492
Metadata
Title
Leveraging Textural Features for Recognizing Actions in Low Quality Videos
Authors
Saimunur Rahman
John See
Chiung Ching Ho
Copyright Year
2017
Publisher
Springer Singapore
Book
9th International Conference on Robotic, Vision, Signal Processing and Power Applications

Print ISBN: 978-981-10-1719-3

Electronic ISBN: 978-981-10-1721-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-1721-6_26