A human activity recognition framework in videos using segmented human subject focus
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-023-03256-4.pdf
Reference50 articles.
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2. Zeng, M., et al.: Convolutional Neural Networks for human activity recognition using mobile sensors. In: Proc. 2014 6th Int. Conf. Mob. Comput. Appl. Serv. MobiCASE 2014, vol. 6, pp. 197–205, (2015). https://doi.org/10.4108/icst.mobicase.2014.257786.
3. Dhiman, C., Vishwakarma, D. K.: A review of state-of-the-art techniques for abnormal human activity recognition. Eng. Appl. Artif. Intell., 77( June 2018), 21–45 (2019). https://doi.org/10.1016/j.engappai.2018.08.014.
4. Dhiman, C., Vishwakarma, D. K., Agarwal, P.: Part-wise spatiooral attention driven CNN-based 3D human action recognition. ACM Trans. Multimed. Comput. Commun. Appl., 17(3) (2021). https://doi.org/10.1145/3441628.
5. Dhiman, C., Vishwakarma, D. K.: View-invariant deep architecture for human action recognition using two-stream motion and shape temporal dynamics. IEEE Trans. Image Process. 29, 3835–3844 (2020). https://doi.org/10.1109/TIP.2020.2965299.
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