Table 1.
State-of-the-art methods and their interpretation.
| Authors | Methods | Interpretation |
|---|---|---|
| AlbuSlava 2016 [13] and Majed Latah 2017 [14] | 3D CNN | Spatial features |
| Murad and Ryun 2017 [15] and Qin et al. [16] | Deep recurrent neural networks and multimodal sensors | Motion features |
| Ning et al., 2017 [17] | Local optical flow of a global human silhouette | Motion features |
| Nicolas et al., 2016 [18] | GRU + RCN | Spatio-temporal features |
| Xu et al., 2016 [19] and Baldominos et al. [20] | RCNN | Spatio-temporal features |
| Zhang et al., 2016 [21] | Vector of locally aggregated descriptors, SIFT and ISA | Spatio-temporal features |
| Zhao et al., 2017 [22] | RNN + GRU + 3D CNN | Spatio-temporal features |
| Faria et al., 2012 [23] | Dynamic Bayesian mixture model | Skeleton features |
| Koppula et al., 2013 [24] | HMM | Skeleton features |
| Bingbing et al., 2013 [25] | Histogram of oriented gradient and SVM | Spatio-temporal features |
| Wang et al., 2014 [26] | LOM | Skeleton features |
| Shan and Akella 2014 [27] and Enea et al., 2016 [28] | Pose Kinetic Energy + SVM | Skeleton features |
| Gaglio et al., 2015 [29] | Kmeans + HMM + SVM | Skeleton features |
| Manzi et al.,2017 [30] | Kmeans + Sequential Minimal Optimization | Skeleton features |
| Srijan et al., 2018 [31], Cruz et al. [32] and Khaire et al. [33] | RGB-D + CNN + LSTM model | Skeleton and contextual features |
| Yanli et al., 2018 [34] | VS-CNN | Skeleton and contextual features |
| Hug et al., 2019 [35] | The conversion of the distance value of two joints to colors points + CNN | Skeleton and contextual features |
| Proposed approach | CNN (Inception V3 + mobileNet) + GRU + RNN + Kalman filter | Skeleton + spatio-temporal features |
CNN: Convolutional Neural Network, GRU: Gated Recurrent Units, LOM: Local Occupation Model, LSTM: Long Short Term Memory, RCN: Recurrent Convolution Networks, RNN: Recurrent Neural Network, SVM: Support Vector Machines, VS-CNN: View-guided Skeleton-CNN.