# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 4 Performance Evaluations
### Training Procedure  
Different datasets were used for training and testing, based on the computational characteristics of each model, to simulate real-world conditions and evaluate performance under mobile deployment constraints. The R(2+1)D and VST models were trained and evaluated using the original dataset, recorded at 30 fps. Since both models leverage complex spatio-temporal feature extraction mechanisms, using the full frame rate enabled them to retain the temporal information necessary for accurate sign language recognition. The 3D CNN and MViT models were trained and evaluated using the secondary dataset. For MViTs, frames were systematically dropped to simulate real-time conditions on mobile devices, optimizing the model for environments with lower frame rates. The 3D CNN model is typically used with 16-frame inputs, so it was evaluated using this frame size to align with its standard architecture and ensure optimal performance under these \begin{table}
\begin{tabular}{l l l} \hline \hline
**Models** & **Key Features** & **Computational Efficiency** \\ \hline \multirow{3}{*}{3D CNN} & This is a traditional CNN-based model that extends & \\  & 2D convolutions to spatio-temporal feature extraction, & \\  & effectively capturing continuous motion and gestures & \\  & in sign language videos. & \\ \hline \multirow{3}{*}{R(2+1)D} & This is a factorized version of 3D CNN that & \\  & decomposes 3D convolutions into separate spatial & \\  & and temporal components, optimizing both & \\  & computation and model capacity without adding & \\  & extra parameters. Additionally, ReLU activations in & \\  & residual blocks further enhance nonlinearity. & \\ \hline \multirow{3}{*}{VST} & A transformer-based model that employs hierarchical & \\  & self-attention with shifted windows to capture & \\  & long-range dependencies in video sequences, making & \\  & it ideal for dynamic sign language recognition tasks. & \\ \hline \multirow{3}{*}{MViT} & A mobile-optimized transformer architecture & \\  & featuring efficient multi-scale self-attention & \\  & mechanisms for real-time processing, balancing & \\  & lightweight deployment with robust feature & \\  & extraction. & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overview and comparison of selected video recognition models for sign language recognition. conditions. Similarly, for MViTs, since they are designed for mobile environments, 16-frame input data was used, just like with 3D CNN models. By employing different training strategies tailored to each model's computational efficiency and intended deployment scenario, this study ensures a fair and practical evaluation of sign language recognition performance in both high-resource and real-time mobile environments. In other words, this study considers high-performance computing environments with a high frame rate where 30 frames of image data can be used to generate a single input clip, providing relatively high performance. Additionally, it considers mobile computing environments with lower computational performance, where only 16 frames of image data can be used to generate a single input clip. A set of common hyperparameters, as shown in Table 8, was applied to maintain consistency in training across the different models. As shown in Table 8, the batch size was set to 4 for the Video Swin Transformer, 3D CNN, and R(2+1)D models, whereas MViT used a batch size of 2 to accommodate its more lightweight design. The input image resolution was 224 \(\times\) 224 pixels for the transformer-based models (VST, MViT) and 112 \(\times\) 112 pixels for the CNN-based models (3D CNN and R(2+1)D), considering their respective architectures. The number of frames per video clip varied across models, with VST and R(2+1)D using 30-frame clips, while 3D CNNs and MViTs were trained on 16-frame clips, balancing computational cost and temporal information capture.