Human action recognition based on the Grassmann multi-graph embedding
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
http://link.springer.com/article/10.1007/s11760-018-1354-1/fulltext.html
Reference53 articles.
1. Wu, D., et al.: Recent advances in video-based human action recognition using deep learning: a review. In: International Joint Conference on Neural Networks (IJCNN), pp. 2865–2872, May 2017
2. Hou, R., et al.: Tube convolutional neural network (T-CNN) for action detection in videos. In: IEEE International Conference on Computer Vision, pp. 5822–5831 (2017)
3. Li, C., et al.: Deep spatio-temporal manifold network for action recognition. arXiv preprint arXiv:1705.03148 , pp. 1–12, May 2017
4. Weinzaepfel, P., et al.: Learning to track for spatio-temporal action localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3164–3172 (2015)
5. Wang, Y., et al.: Two-stream SR-CNNs for action recognition in videos. In: Proceedings of British Machine Vision Conference (BMVC), pp. 108.1–108.12, Sept 2016
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