A novel algorithm for human action recognition in compressed domain using attention-guided approach

Author:

Praveenkumar S. M.,Patil Prakashgoud,Hiremath P. S.

Publisher

Springer Science and Business Media LLC

Subject

Information Systems

Reference35 articles.

1. Wu, C.-Y., Zaheer, M., Hu, H., Manmatha, R., Smola, A.J., Krähenbühl, P.: Compressed video action recognition. In: CVPR, (2018)

2. Bommes, L., Lin, X., Zhou, J.: MVmed: fast multi-object tracking in the compressed domain. In: 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1419-1424 (2020), https://doi.org/10.1109/ICIEA48937.2020.9248145

3. Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: 2011 Int. Conf. Comput. Vis., pp. 2556–2563, IEEE, (2011)

4. Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. tech. rep., University of Central Florida, (2012)

5. Jiang, Y.-G., Liu, J., Roshan Zamir, A., Toderici, G., Laptev, I., Shah, M., Sukthankar, R.: THUMOS challenge: action recognition with a large number of classes. http://crcv.ucf.edu/THUMOS14/, (2014). Accessed July 2022

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