# Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model
## 4 Results
### Results and Performance of YOLOv3  
Figure 7 shows the output of the developed application that performs real-time hand gesture recognition. As you can see, the bounding box is a little bit large, which is because it was left intentionally as 416 \(\times\) 416 for covering the maximum part, then scattered, and all unnecessary information was removed from the image so we could obtain a larger coverage area. This also helps in the zoom case when the object is too large, as it will cleverly identify which gesture is being represented, and it then returns the class ID with the best match.  
Figure 8 shows the training performance of the proposed deep learning model. The results are 98% correct, as measured by experiments. The experiment is in real time because we trained our model with YOLO and Pascal VOC [46] configurations and tested it with live images. We added the YOLO-annotated labels into a CSV file and re-ran training tests to confirm the accuracy of the model by plotting the curve, as we can find the accuracy of an individual gestation in the real-time experiment. The fault was seen in the transitions between one gesture to another, because the application is in real-time, so the system can detect dynamic objects too, as proven by our experiments.  
We used several different algorithms to test which was the best method for the gesture recognition application. We compared YOLOv3 [47] with other deep learning models  
Figure 8: The performance of the proposed model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper Parameter** & **Value** \\ \hline Learning Rate & 0.001 \\ Epochs & 30 \\ No. of Classes & 5 \\ Algorithm & DarkNet-53 and YOLOv3 \\ Optimizer & Adam \\ Activation & Linear, Leaky \\ Filter Size & [64,128,256,512,1024] \\ Mask & 0–8 \\ Decay & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters and values for environment setup.  
Figure 7: Gesture detected using the proposed YOLOv3-based methodology.  
(VGG16 [48] and SSD [49]). In the end, YOLOv3 produced the best results and was chosen, with an accuracy of 97.68% during training and an outstanding 96.2% during testing. DarkNet-53 was used to train the dataset, and for the detection, YOLOv3 was used. As we know, in real-time experiments it is not quite possible to replicate the gestures exactly, so in order to determine the accuracy of the model, so we generated a CSV file of a few YOLO-annotated labels and re-ran the code with some modifications for the accuracy curve.  
Table 4 explains the accuracy of the individual models, where the stochastic gradient descent gave the lowest value because the real-time motion stochastic could not identify moving objects correctly, compared to the other algorithms. We evaluated the performance of deep learning models using the precision, recall, F-1 score, and accuracy measures, achieving an accuracy of 97.68% for YOLOv3.  
Table 5 shows the comparison of the state-of-the-art works of [PERSON] et al. [28], [PERSON] et al. [31], [PERSON] et al. [32], [PERSON] et al. [50], [PERSON] et al. [51], and [PERSON] et al. [52] with our proposed model, which produced better results with higher accuracy.