Action recognition and tracking via deep representation extraction and motion bases learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-021-11888-8.pdf
Reference34 articles.
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2. Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271
3. Caron M, Bojanowski P, Joulin A, Douze M (2018) Deep clustering for unsupervised learning of visual features. In: Proceedings of the European conference on computer vision (ECCV), pp 132–149
4. Chavarriaga R, Sagha H, Calatroni A, Digumarti ST, Tröster G, Millán JdR, Roggen D (2013) The opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern Recogn Lett 34(15):2033–2042
5. Chen C, Lu X, Markham A, Trigoni A (2018) Ionet: Learning to cure the curse of drift in inertial odometry. arXiv:abs/1802.02209
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