Modeling Trajectories for 3D Motion Analysis-Reference-Cited by-同舟云学术

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Modeling Trajectories for 3D Motion Analysis

Published:2020 Issue: Volume: Page:409-429
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Elaoud Amani,Barhoumi Walid,Drira Hassen,Zagrouba Ezzeddine

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-41590-7_17

Reference95 articles.

1. Mitchel, T., Ruan, S., Gao, Y., Chirikjian, G.: The globally optimal reparameterization algorithm: an alternative to fast dynamic time warping for action recognition in video sequences. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE (2018)

2. Susan, S., Mittal, M., Bansal, S., Agrawal, P.: Dynamic texture recognition from multi-offset temporal intensity co-occurrence matrices with local pattern matching. In: Verma, N., Ghosh, A. (eds.) Computational Intelligence: Theories, Applications and Future Directions-Volume II, pp. 545–555. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1135-2_41

3. Wang, H., Kläser, A., Schmid, C., Cheng-lin, L.: Action recognition by dense trajectories. In: CVPR. IEEE (2011)

4. Islam, S., Qasim, T., Yasir, M., Bhatti, N., Mahmood, H., Zia, M.: Single-and two-person action recognition based on silhouette shape and optical point descriptors. SIViP 12(5), 853–860 (2018)

5. Barhoumi, W.: Detection of highly articulated moving objects by using co-segmentation with application to athletic video sequences. SIViP 9(7), 1705–1715 (2015)

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