2-D Skeleton-Based Action Recognition via Two-Branch Stacked LSTM-RNNs

Action recognition in video sequences is an interesting field for many computer vision applications, including behavior analysis, event recognition, and video surveillance. In this article, a method based on 2D skeleton and two-branch stacked Recurrent Neural Networks (RNNs) with Long Short-Term Mem...

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Veröffentlicht in:	IEEE transactions on multimedia 2020-10, Vol.22 (10), p.2481-2496
Hauptverfasser:	Avola, Danilo, Cascio, Marco, Cinque, Luigi, Foresti, Gian Luca, Massaroni, Cristiano, Rodola, Emanuele
Format:	Artikel
Sprache:	eng
Schlagworte:	2D skeleton Action recognition Artificial neural networks Cameras Computer vision Datasets Feature extraction Indoor environments long short-term memory (LSTM) Neural networks Recognition Recurrent neural networks recurrent neural networks (RNNs) Skeleton Sports Streaming media Three-dimensional displays Two dimensional displays Video data
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