## 4 Results
### Experimental Evaluation and Results
The elements that created problems can be remedied both by adjusting the hardware components and by adding optical filters to reduce FOV (Field of View) and extracting parasitic light, but also by adding new video cameras both in the front of the car and in the side mirrors for a wide spectrum of observation [69]. All these elements make significant adjustments in increasing quality and accuracy, with more sources and more comparison areas. The analysis of all scenarios is highlighted in Table 1 which details the number of frames, travel speed, errors, and accuracy shown by the proposed module. Another not negligible aspect is that HoG features can be implemented for image processing. This would greatly increase the processing performance, and it is imperative to analyze the gradient several times as a linear operator. We can say that the accuracy in the case of  
Figure 6: Unidentified elements and pedestrians, due to driving speed, stray light, or distance from the vehicle. pedestrian detection may have decreased caused by the total number of frames per second and the amount of information to be processed. This can be seen in the table below. The current features that are being worked on improve the detection area of the algorithms by performing an image analysis based on contours and delimitations between the pedestrian area and the road. This step is made to exclude conflicts of any kind by sequencing, and is much more eloquent; based on leaving spaces allocated, the classifications improved, with over 20 different classes of static objects. In the first phase, the localization and reprocessing errors were in line with expectations, but the accuracy and precision were improved by overlapping the convolution layers, obtaining results about 10% more conclusive than in the early stages. The major advantage of the YOLOv3 algorithm is that it uses independent classifiers for class analysis and the binary losses obtained have cross-entropy and this factor identifies the class much more objectively [70; 71]. When such logical and independent classifiers are used, the object created in its own model can be detected from a wide range of other objects. Therefore, the studies carried out led to the use of this algorithm, and the adjustments made it possible to identify over 80 categories of objects, exponentially adjusting the computing power. According to the analyses carried out in the literature, there are other elements that need to increase the effectiveness of the approach in terms of clothing and increased accuracy [72; 73]. Identifying key objects for vulnerable people is also an aspect that we are developing. We can see in the graph in Figure 7 that through small data packets and detailed segments a viable accuracy is obtained following the detection process. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Video Duration** & **Total Number of** **in Seconds** & **Frames** **Bocks Decoded** & **Faces** & **Total** **Lost** & **Pedestrians** & **Pedestrian** & **Travel Speed** & **Detection Accuracy \%** \\ \hline
180 & 10,562 & 44,324 & 2 & 96 & 86 & 15–20 & 89.50\% \\
220 & 9803 & 3876 & 12 & 125 & 91 & 15–25 & 72.80\% \\
260 & 5992 & 2461 & 22 & 128 & 84 & 15–20 & 65.60\% \\
180 & 9762 & 5803 & 31 & 101 & 56 & 35–40 & 55.40\% \\
220 & 7893 & 2876 & 54 & 133 & 76 & 30–40 & 57.10\% \\
280 & 3942 & 1461 & 45 & 98 & 67 & 35–45 & 68.30\% \\
280 & 3162 & 803 & 76 & 176 & 56 & 40–50 & 31.80\% \\
280 & 2403 & 476 & 87 & 105 & 48 & 55–60 & 45.70\% \\
350 & 1242 & 461 & 123 & 178 & 61 & 60–65 & 34.20\% \\
280 & 743 & 324 & 134 & 144 & 54 & 60–65 & 37.50\% \\ \hline \end{tabular}
\end{table} 
Table 1: Identification accuracy details, frames, and pedestrians detected from total people existing in images, frames lost, and detected travel speed. Figure 7: Graphic analysis based on the table of details of identification accuracy, frames and pedestrians detected from the total number of people in the images, frames lost and speed detected.