# Novel Hand Gesture Alert System
## 3 Proposed Model
### Detector and Classifier
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Number of Input** & **\% Accuracy on** & **\% Accuracy on** \\  & **Frames** & **RGB** & **Depth** \\ \hline VGG-16 [47] & 16 & 62.5 & 62.30 \\ VGG-16 + LSTM [47] & 16 & 74.60 & 77.69 \\ Custom 3D CNN & 16 & 85.1 & 87.9 \\ Custom 3D CNN & 24 & 90.3 & 90.6 \\ Custom 3D CNN & 32 & 91 & 91.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Improvement of the custom 3D CNN by increasing the input size and comparison to state-of-the-art techniques applied on the EgoGesture dataset [45]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frames** & **Algorithm 1** & **Algorithm 2** & **CounterGesture** & **Alarm** \\ \hline \hline \multirow{4}{*}{Human detected} & Specific & & \\  & & gesture & +1 & \\  & & detected & & Alarm triggered as \\ \cline{1-1}  & & & & CounterGesture= 3 \\ \cline{1-1}  & & & \\



When processing images from video surveillance, material quality (poor quality of image data, low light level, blur, pixelation of small objects) can be an obstacle; to address it, we propose enhancing the images (Figure 10) before submitting them to the human detector module (Figure 5). Besides, the RGB-D (Depth sensor) frames are examined for different input sizes. Over the course of the experiment, the depth modality has proven to be essential to increase the performance, rather than a simple RGB input. Indeed, the depth component allowed the filtering of the movement from the background and helped to focus more on the movement of the hand, resulting in the discrimination of the features with the depth modality. Figure 10: Preprocessing of the images to ensure a better quality of input images in our system. (**a**) The original image, (**b**) the image on which we apply a normalization function to distribute the pixel intensities from 0 to 255; this will enhance the image without losing information. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frames** & **Algorithm 1** & **Algorithm 2** & **CounterGesture** & **Alarm** \\ \hline Human detected &
\begin{tabular}{c} Normal \\ gesture \\ detected \\ \end{tabular} & 0 & NA \\ Human detected & NA & 0 & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._

## 6 Conclusions

Considering sexual assault, this paper proposes a solution via the use of a new hierarchical architecture with three models for hand gesture alert systems. The proposed architecture enables efficient resource utilization and early detection for essential hand gesture alert applications. We obtained approximate results for both datasets when we evaluated our proposed model. We defined a set of hands gestures that were identified by our classifier, and we introduced a module called \"counterGesture\". The latter allowed us to count the number of occurrences of a predefined gesture and trigger the alert. Besides, we found that the training time was far too long on the Jest dataset at a learning rate varying between 0.0001 and 0.001. We anticipate that in our future work, we will associate the facial expression with the alert decision in order to investigate ResNext [48; 49; 50; 51] or a faster impact of the CNN-Region mode, at the same time as the detector and classifier [52; 53]. Further study should consider the combination of lighter Deep neural networks (DNNs) to maintain accuracy and improve speed. Conceptualization, S.M., O.K., A.S. and K.K. ; methodology, O.K., P.M. and K.K. ; software, S.M. and O.K. ; validation, O.K., S.M. and P.M.; formal analysis, A.S., S.M. ; investigation, S.M. ; resources, O.K., K.K. and P.M.; data curation, S.M., A.S. and O.K. ; writing--original draft preparation, S.M. and O.K. ; writing--review and editing, S.M., O.K., and A.S.; visualization, S.M., O.K.,; supervision, A.S., K.K., P.M. and O.K. ; project administration, O.K., K.K. and P.M.; funding acquisition, O.K. and K.K.