# Cascading Pose Features with CNN-LSTM for Multiview Human Action Recognition
## 2 Related Work
A CNN's representational capability is typically determined by its depth, and an enhanced feature set ranging from simple to complex abstractions can aid in the learning of complicated issues. The falling gradient is the main problem that deep architectures have to deal with. At first, researchers tried to solve this problem by connecting intermediate layers to auxiliary learners [21]. In 2015, making new connections to speed up the convergence rate of deep CNN architectures was the most promising area of study. In this area, different ideas have been put forward, such as information gating mechanisms acrossmultiple layers, skip connections, and cross-layer channel connectivity [22; 23]. Modern deep architectures, such as VGG, ResNet, etc., performed well in several experiments, not only for classification issues but also for challenging recognition and localization issues such as semantic and instance-based object segmentation, scene parsing, scene placement, etc. In the same way, many interesting detection algorithms, such as Feature Pyramid Networks, Cascade R-CNN, Libra R-CNN, etc., changed the architectures mentioned above to make them work better [24]. Combining these networks with a recurrent neural network (RNN) made it possible for deep CNN to be used for image captioning. However, the high cost of computation and the need for a lot of memory are two main problems with deep learning architectures. Because of this, it is very hard to use deep CNN models in environments with few resources. Conventional convolution operations require a very large number of multiplications. This makes the inference time longer and limits CNN to applications with limited memory and time. Many real-world applications, such as self-driving cars, robots, healthcare, and mobile apps, do tasks that need to be done on platforms with limited computing power in a timely way. A number of studies have used deep learning architecture for HAR as illustrated in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Methods** & **Datasets** & **Performance** \\ \hline [PERSON] et al. [21] & CNN and RNN & KTH & 94.39 \\ \hline [PERSON] et al. [22] & 3 DCNN & KTH & 90.02 \\ \hline [PERSON] et al. [23] & LSTM & KTH & 90.7 \\ \hline [PERSON] et al. [24] & Factorised spatio-temporal CNN & HMDB-51 & 59.1 \\ \hline [PERSON] et al. [25] & two stream CNN & HMDB-51 & 59.4 \\ \hline [PERSON] et al. [26] & CNN & HMDB-51 & 65.9 \\ \hline [PERSON] et al. [27] & DB-LSTM & HMDB-51 & 87.64 \\ \hline [PERSON] et al. [28] & LSTM-CNN & HMDB-51 & 55.3 \\ \hline [PERSON] et al. [29] & MV-CNN & UCF101 & 86.4 \\ \hline [PERSON] et al. [30] & LSTM with 30 frame unroll & UCF101 & 88.6 \\ \hline [PERSON] et al. [31] & SP-CNN & UCF101 & 91.6 \\ \hline [PERSON] et al. [32] & Rank Pooling +CNN & Hollywood2 & 75.2 \\ \hline [PERSON] et al. [33] & Features (Pose-based) & MSR-action3D & 90 \\ \hline [PERSON] et al. [34] & Pose-based CNN & MPII Cooking & 71.4 \\ \hline [PERSON] et al. [35] & Cuboids & MCAD & 56.8 \\ \hline [PERSON] et al. [36] & Covariance matrices & MCAD & 64.3 \\ \hline [PERSON]. [PERSON] et al. [35] & STIP & MCAD & 81.7 \\ \hline [PERSON]. [PERSON] et al. [37] & IDT & MCAD & 84.2 \\ \hline [PERSON]. [PERSON] et al. [27] & \
\begin{tabular}{c} Conflux LSTM \\ network \\ \end{tabular} & MCAD & 86.9 \\ \hline [PERSON] et al. [38] &
\begin{tabular}{c} OpenPose + \\ FineKNN \\ \end{tabular} & MCAD & 86.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review on Deep learning-based techniques for Human Action Recognition. In view of the limited performance of the methods discussed above, this work proposes a simpler strategy on a 2D dataset that can be used to cope with existing real-time systems. To avoid the time-consuming process of converting skeletal joints data to image sequences and then training them using an image-based classifier that extracts the features again, we suggest that the skeletal joints data be used directly for training and testing of the HAR system.