Real-time human action recognition from motion capture data

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Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “Vantigodi and Babu [137] Variance of Joints Stat Lowlv Hand X X Zhao et al.”
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates (2017, methodology): “Geometric 95.4% Vantigodi et al. [90] Geometric 96.1% Vantigodi et al. [91] Geometric 97.6% Kapsouras et al. [92] Geometric 98.2% Hierarchical RNN [30] Geometric 100% Co-occurrence LSTM [48] Geometric 100% ST-LSTM (Tree) + Trust Gate Geometric 100% We adopt the experimental protocol in [30] on the…”
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition (2016, neutral): “…Skeleton Joint Features [67] 87.9% Lie Group [7] (reported by [68]) 93.6% Elastic functional coding [68] 94.9% ST-LSTM (Tree) + Trust Gate 95.0% [70] 96.1% Oﬂi et al. [71] 95.4% Vantigodi et al. [72] 97.6% Kapsouras et al. [73] 98.2% HBRNN [30] 100% ST-LSTM (Tree) + Trust Gate 100% We measure…”
Skeleton based action recognition with convolutional neural network (2015, result): “In contrast to those handcrafted features based approaches [6, 13, 17, 18], our approach, like [1], is an effective end-to-end solution to skeleton based action recognition.”
Hierarchical recurrent neural network for skeleton based action recognition (2015, unknown): “[30], Vantigodi et al .”