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Recognizing Human Actions in Basketball Video Sequences on the Basis of Global and Local Pairwise Representation

Recognizing Human Actions in Basketball Video Sequences on the Basis of Global and Local Pairwise Representation

Masaki Takahashi, Masahide Naemura, Mahito Fujii, James J. Little
Copyright: © 2014 |Volume: 5 |Issue: 3 |Pages: 19
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781466655638|DOI: 10.4018/ijmdem.2014070102
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MLA

Takahashi, Masaki, et al. "Recognizing Human Actions in Basketball Video Sequences on the Basis of Global and Local Pairwise Representation." IJMDEM vol.5, no.3 2014: pp.28-46. http://doi.org/10.4018/ijmdem.2014070102

APA

Takahashi, M., Naemura, M., Fujii, M., & Little, J. J. (2014). Recognizing Human Actions in Basketball Video Sequences on the Basis of Global and Local Pairwise Representation. International Journal of Multimedia Data Engineering and Management (IJMDEM), 5(3), 28-46. http://doi.org/10.4018/ijmdem.2014070102

Chicago

Takahashi, Masaki, et al. "Recognizing Human Actions in Basketball Video Sequences on the Basis of Global and Local Pairwise Representation," International Journal of Multimedia Data Engineering and Management (IJMDEM) 5, no.3: 28-46. http://doi.org/10.4018/ijmdem.2014070102

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Abstract

A feature-representation method for recognizing actions in sports videos on the basis of the relationship between human actions and camera motions is proposed. The method involves the following steps: First, keypoint trajectories are extracted as motion features in spatio-temporal sub-regions called “spatio-temporal multiscale bags” (STMBs). Global representations and local representations from one sub-region in the STMBs are then combined to create a “glocal pairwise representation” (GPR). The GPR considers the co-occurrence of camera motions and human actions. Finally, two-stage SVM classifiers are trained with STMB-based GPRs, and specified human actions in video sequences are identified. An experimental evaluation of the recognition accuracy of the proposed method (by using the public OSUPEL basketball video dataset and broadcast videos) demonstrated that the method can robustly detect specific human actions in both public and broadcast basketball video sequences.

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