Abstract
We propose a Bag-of-Words (BoW) based technique for human action recognition in videos containing challenges like illumination changes, background changes and camera shaking. We build the pose descriptors corresponding to the actions, based on the gradient-weighted optical flow (GWOF) measure, to minimize the noise related to camera shaking. The pose descriptors are clustered and stored in a dictionary of poses. We further generate a reduced dictionary, where words are termed as pose duplet. The pose duplets are constructed by a graphical approach, considering the probability of occurrence of two poses sequentially, during an action. Here, poses of the initial dictionary, are considered as the nodes of a weighted directed graph called the duplet graph. Weight of each edge of the duplet graph is calculated based on the probability of the destination node of the edge to appear after the source node of the edge. The concatenation of the source and destination pose vectors is called pose duplet. We rank the pose duplets according to the weight of the edge between them. We form the reduced dictionary with the pose duplets with high edge weights (called dominant pose duplet). We construct the action descriptors for each actions, using the dominant pose duplets and recognize the actions. The efficacy of the proposed approach is tested on standard datasets.
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References
Ziaeefar, M., Bergevin, R.: Semantic human activity recognition: a literature review. Pattern Recogn. 48(8), 2329–2345 (2015). doi:10.1016/j.patcog.2015.03.006
Shechtman, E., Irani, M.: Space-time behavior based correlation. In: Computer Vision and Pattern Recognition (CVPR), pp. 405–412. IEEE Press (2005)
Wang, J., Xu, Z.: STV-based video feature processing for action recognition. Sig. Process. 93(8), 2151–2168 (2012)
Laptev, I., Marszaek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies, In: Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)
Beaudry, C., Peteri, R., Mascarilla, L.: Action recognition in videos using frequency analysis of critical point trajectories. In: International Conference on Image Processing (ICIP), pp. 1445–1449. IEEE Press (2014)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)
Chaudhry, R., Ravichandran, A., Hager, G., Vidal, R.: Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: Computer Vision and Pattern Recognition (CVPR), pp. 1932–1939. IEEE Press (2009)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893. IEEE Press (2005)
Mukherjee, S., Biswas, S.K., Mukherjee, D.P.: Recognizing human action at a distance in video by key poses. IEEE Trans. Circuits Syst. Video Technol. 21(9), 1228–1241 (2011)
Mukherjee, S., Biswas, S.K., Mukherjee, D.P.: Recognizing interactions between human performers at a distance by ‘Dominating Pose Doublet’. Mach. Vis. Appl. 25(4), 1033–1052 (2014)
Wang, H., Schmid, C.: Action recognition with improved trajectories. In: International Conference on Computer Vision (ICCV), pp. 3551–3558. IEEE Press (2013)
Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006)
Wolf, C., Mille, J., Lombardi, L.E., Celiktutan, O., Jiu, M., Baccouche, M., Dellandrea, E., Bichot, C., Garcia, C.-E., Sankur, B.: Evaluation of video activity localizations integrating quality and quantity measurements. Comput. Vis. Image Underst. 127, 14–30 (2014)
Ryoo, M.S., Aggarwal, J.K.: UT-interaction dataset, ICPR contest on semantic description of human activities (SDHA) (2010). http://cvrc.ece.utexas.edu/SDHA2010/Human_Interaction.html
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Mukherjee, S. (2015). Human Action Recognition Using Dominant Pose Duplet. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_44
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DOI: https://doi.org/10.1007/978-3-319-20904-3_44
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