Abstract
Human action recognition has gained popularity because of its wide applicability in automatic retrieval of videos of particular action using visual features. An approach is introduced for human action recognition using trajectory-based spatiotemporal descriptors. Trajectories of minimum Eigen feature points help to capture the important motion information of videos. Optical flow is used to track the feature points smoothly and to obtain robust trajectories. Descriptors are extracted around the trajectories to characterize appearance by Histogram of Oriented Gradient (HOG), motion by Motion Boundary Histogram (MBH). MBH computed from differential optical flow outperforms for videos with more camera motion. The encoding of feature vectors is performed by bag of visual features technique. SVM with nonlinear kernel is used for recognition of actions using classification. The performance of proposed approach is measured on various datasets of human action videos.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Online Video Consumption in India has Doubled in the Past 2 Years, https://www.comscore.com/Insights/Press-Releases/2013/5/Online-Video-Consumption-in-India-May-2013
Jiménez, M., Yeguas, E., Blanca, N.: Exploring STIP-Based Models for Recognizing Human interactions in TV videos. Pattern Recognition Letters, Vol. 34. Elsevier (2013) 1819–1828
Slimani, K., Benezeth, Y., Souami, F.: Human Interaction Recognition Based on the Co-occurrence of Visual Words. Computer Vision and Pattern Recognition Workshops, IEEE (2014)
Nguyen, N., Yoshitaka, A.: Human Interaction Recognition using Independent Subspace Analysis Algorithm. International Symposium on Multimedia (ISM), IEEE (2014) 40–46
Dhamsania, C., Ratanpara, T.: A Survey on Human Action Recognition from Videos. International Conference on Innovations in Information Embedded and Communication Systems, IEEE (2016)
Jiménez, M., Blanca, N.: Human Interaction Recognition by Motion Decoupling. Pattern Recognition and Image Analysis, Springer Berlin Heidelberg (2013) 374–381
Shi, J., Tomasi, C.: Good Features to Track. Computer Vision and Pattern Recognition, IEEE (1994) 593–600
Horn, B., Schunck, B.: Determining Optical Flow. Artificial Intelligence, Vol. 17, (1981)
Wang, H., Kläser, A., Schmid, C., Liu, C.: Action Recognition by Dense Trajectories. Computer Vision and Pattern Recognition, IEEE (2011) 3169–3176
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, IEEE (2005) 886–893
Laptev, I., Marszałek, M., Schmid, C., Rozenfeld, B.: Learning Realistic Human Actions from Movies. Computer Vision and Pattern Recognition, IEEE (2008) 1–8
Dalal, N., Triggs, B., Schmid, C.: Human Detection using Oriented Histograms of Flow and Appearance. Computer Vision–ECCV, Springer Berlin Heidelberg (2006) 428–441
Yang, J., Jiang, Y., Hauptmann, A., Ngo, C.: Evaluating Bag-of-Visual-Words Representations in Scene Classification. IN: ACM SIGMM Int’l Workshop on Multimedia Information Retrieval (MIR 2007), Augsburg, Germany (2007)
UT-Interaction Dataset, ICPR Contest on Semantic Description of Human Activities (SDHA), http://cvrc.ece.utexas.edu/SDHA2010/Human_Interaction.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dhamsania, C., Ratanpara, T. (2017). Human Action Recognition Using Trajectory-Based Spatiotemporal Descriptors. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_1
Download citation
DOI: https://doi.org/10.1007/978-981-10-3153-3_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3152-6
Online ISBN: 978-981-10-3153-3
eBook Packages: EngineeringEngineering (R0)