# A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare
## 4 Implementation Details and Results
### Performance Evaluation
#### 4.4.2 Implementation Results  
Figure 14 is a frame-by-frame graph of the accuracy rate when our implementation is operated using the Selfie and DeepLab-v3 segmentation models, respectively. In Figure 14, the numbers on the x-axis represent the order in the series of input frames, and the values on the y-axis show the accuracy rates for those input frames, which have been measured according to the methods in Section 4.4.1. The brightness levels in the index  
Figure 14: The proposed system’s accuracy was measured with our implementation: (**a**) Selfie model case; (**b**) DeepLab-v3 model case. Figure 13: Result of regional brightness control: (**a**) projection of an original image with a mannequin; (**b**) projection image including a brightness control area. mean that our measurements have been carried out by increasing the brightness from 0% to 100% in 20% intervals. In Figure 14a, the accuracy rates with the selfie model show a gradually decreasing trend as the number of frames increases, except in the case of the brightness level of 100%. In Figure 14b, the accuracy rates with the DeepLab-v3 model appear to be over 70% in most cases, except for the brightness level of 0%. The results in Figure 14 illustrate that the accuracy rates are more stable with the Deeplab-v3 model compared to the case of the Selfie model. We have also measured the _FN_ rates and the _FP_ rates to analyze the differences between the cases further using those two segmentation models. Figure 15 shows a frame-by-frame graph of the _FN_ rates measured with our implementation. In Figure 15a, the _FN_ rates of the Selfie model case generally appear to be small values (around 0) and do not have many changes. When the brightness level is set to 0%, the _FN_ rates considerably increase since the segmentation model does not work appropriately with much lower brightness. As shown in Figure 15b, the _FN_ rates of the DeepLab-v3 model case also appear stable below 0.05 at most brightness levels except for level 0%. This implies that the accuracy rate differences of both segmentation models in our implementation are not significantly affected by the _FN_ errors. Figure 16 is a frame-by-frame graph of the _FP_ rates when our implementation is operated using the Selfie and DeepLab-v3 segmentation models. In Figure 16a, the _FP_ rates of the Selfie model case continuously increase as the frame progresses. This may be caused by the fact that the dark area generated by the regional brightness control is provided with a feed-back for processing the human body segmentation of the next frame. When the brightness level is high, 80% through 100%, the _FP_ rate plots are quite stable under 0.05. At a brightness level of 60%, the _FP_ rate plots become unstable between 0.0 and 0.3. As the brightness level decreases, the _FP_ error rates appear significant, so the proposed system does not work properly. As shown in Figure 16b, the _FP_ rates of the DeepLab-v3 model case appeared stable, with values lower than 0.3 in most cases of brightness levels. Likewise, in this case, the changes in _FP_ rates become more active as the brightness level decreases. When reviewing the operational results of the proposed system, several implications can emerge. The measured results of our implementation seemed highly dependent on the ability of the segmentation model. The Selfie model of MediaPipe did not work well without sufficient light. With brightness levels below 60%, it could not properly distinguish the human body and blurred areas as well. Otherwise, the DeepLab-v3 model did not suffer much from low light levels. It is important that more experiments with various segmentation models and experimental scenarios be conducted to increase the proposed system's practicality. This is because the results presented in this paper depend quite a bit on several factors, including the brightness level and features of the previous frame. Figure 15: FN rates of the proposed system: (**a**) Selfie model case; (**b**) DeepLab-v3 model case.