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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
Laptev I, Marszałek M, Schmid C (2008) Rozenfeld B Learning realistic human actions from movies. In: IEEE CVPR, pp 1–8
Kläser A, Marszałek M, Schmid C (2008) A spatio-temporal descriptor based on 3d-gradients. In: BMVC, pp 275–1
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
Mattivi R, Shao L (2009) Human action recognition using LBP-TOP as sparse spatio-temporal feature descriptor. In: Proceedings of CAIP, pp 740–747
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
Wang H, Kläser A, Schmid C, Liu CL (2011) Action recognition by dense trajectories. In: IEEE CVPR, pp 3169–3176
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
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
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)
Laptev I (2005) On space-time interest points. Int J Comput Vis 64(2–3):107–123
Wang H, Schmid C (2013) Action recognition with improved trajectories. In: ICCV, IEEE, pp 3551–3558
Zhao G, Pietikainen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE PAMI 29(6):915–928
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
Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans PAMI 28(12):2037–2041
Ojansivu V, Heikkilä J (2008) Blur insensitive texture classification using local phase quantization. In: Image and signal processing. Springer, pp 236–243
Kannala J, Rahtu E (2012) Bsif: Binarized statistical image features. In: 2012 21st International Conference on Pattern recognition (ICPR), pp 1363–1366
Päivärinta J, Rahtu E, Heikkilä J (2011) Volume local phase quantization for blur-insensitive dynamic texture classification. In: Image analysis, pp 360–369
Vedaldi A, Zisserman A (2012) Efficient additive kernels via explicit feature maps. IEEE PAMI 34(3):480–492
Acknowledgments
This work is supported, in part, by MOE Malaysia under Fundamental Research Grant Scheme (FRGS) project FRGS/2/2013/ICT07/MMU/03/4.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Rahman, S., See, J., Ho, C.C. (2017). Leveraging Textural Features for Recognizing Actions in Low Quality Videos. In: Ibrahim, H., Iqbal, S., Teoh, S., Mustaffa, M. (eds) 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 398. Springer, Singapore. https://doi.org/10.1007/978-981-10-1721-6_26
Download citation
DOI: https://doi.org/10.1007/978-981-10-1721-6_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1719-3
Online ISBN: 978-981-10-1721-6
eBook Packages: EngineeringEngineering (R0)