## 3 Proposed Methods
### Overview of Model  
Here, we present an overview of the methods used in this paper and the obtained results. We introduce a novel method with which to compare the existing model with the results obtained from previous experiments, where the GRU model is a type of Recurrent Neural Network (RNN) architecture that is utilized for image and video classification tasks. Having been originally developed as an extension of the standard RNN model, the GRU introduces gating mechanisms to effectively capture and propagate information over sequential data. In the context of image and video classification, the GRU model can be applied sequentially to process the frames or patches of an image or video. This makes the process faster and less memory-consuming, and this model is shown in Figure 5 and described in the equation are given below, where \(z_{t},r_{t}\) is the reset and update gate, respectively. Meanwhile, \(h_{t}\) is the hidden state. \[z_{t}=\sigma(w_{h_{t}}h_{t}+w_{x_{t}}x_{t}+b_{t}) \tag{1}\]  
\[r_{t}=\sigma(W_{h_{t}}h_{t-1}+W_{x_{t}}x_{t}+b_{r}) \tag{2}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** &
\begin{tabular}{c} **Length** \\ **(S)** \\ \end{tabular} & **Width and Height** & **Class** & **Total Video** \\ \hline Cloth with collision & 30 & 224 \(\times\) 224 & 5 & 1275 \\ Cloth falling dropdown & 30 & 224 \(\times\) 224 & 5 & 1275 \\ Cloth affected by airflow & 30 & 224 \(\times\) 224 & 5 & 1455 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset used for the video classification experiment. Figure 4: Animated videos depicting various movements captured from the video dataset. These movements include: (**a**) cloth with collision; (**b**) cloth falling dropdown; and (**c**) cloth affected by airflow. \[\hat{h}_{t}=\Phi(W_{h}(r_{r}\oplus h_{t-1})+W_{x_{x}}x_{t}+b) \tag{3}\] \[h_{t}=(1-z_{t})\oplus h_{t-1}+z\oplus\hat{h}_{t} \tag{4}\]  
The GRU does not have direct control over memory content exposure whereas LSTM does have such direct control by having an output gate, as show in Figure 6. These two models differ in how they update the memory nodes. LSTM updates its hidden state by summation overflow after the input gate and forget gate. However, GRU assumes a correlation between how much to keep from the current state and how much to obtain from the previous state, and it models this with the \(z_{t}\) gate. As mentioned above, LSTM uses a gated structure, where each gate controls the flow of a particular signal. Each LSTM node has three gates: input, output, and forget gates, each with learnable weights. These gates can learn the optimal way to remember useful information from previous states and thus decide upon the current state. Equations (5)-(7) shows the procedure of computing different gates and hidden states, where \(i_{t}\), \(f_{t}\), and \(o_{t}\) are the input, forget, and output gates, respectively, Equation (9) while \(c_{t}\) denotes the cell's internal state and Equation (10) \(h_{t}\) is the hidden state. \[i_{t}=\sigma\big{(}W_{x_{x}}x_{t}+W_{h_{t}}h_{t-1}+b_{i}\big{)} \tag{5}\]  
Figure 5: Gated Recurrent Unit Architecture. Figure 6: The architecture utilizes a recurrent full convolutional network with a GRU layer. The image is sequentially fed frame by frame into the network, which consists of a Conv-GRU layer that is applied to the feature maps generated by the preceding network at each frame. * signifies pointwise multiplication. The resulting output is then passed through an additional convolutional layer to generate heat maps. Finally, a deconvolution layer is used to up-sample the heat map to the desired spatial size. \[f_{t} =\sigma\Big{(}W_{x_{f}}x_{t}+W_{h_{f}}h_{t-1}+b_{f}\Big{)} \tag{6}\] \[o_{t} =\sigma(W_{xo}x_{t}+W_{ho}h_{t-1}+b_{o})\] (7) Figure 4:0 (8) Figure 4:1 (9) Figure 4:2  
For video classification, the LSTM model follows a sequential processing approach, similar to image classification.