# Using Computer Vision to Collect Information on Cycling and Hiking Trails Users
## 2 Related Work
### Analysis of Studies  
This section presents an analysis of the projects and works related to the topic covered in this article, which resulted from the previous selection process. Table 1 summarizes the main characteristics considered in this analysis (title of the study, year of publication, model used, dataset used, methodologies used, and results). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Study** & **Year** & **Dataset** & **Methodologies** & **Results** \\ \hline \multirow{2}{*}{[14]} & Vehicle Counting on & \multirow{2}{*}{2023} & Dataset created by & YOLOv8, & \multirow{2}{*}{mAP of 82.9\%} \\  & Vietnamese Street & & the author & StrongSORT & \\ \hline \multirow{4}{*}{[15]} & Development of & \multirow{4}{*}{2023} & \multirow{4}{*}{MS-COCO} & \multirow{4}{*}{Mask R-CNN,} & \multirow{2}{*}{\begin{tabular}{c} mAP of 100\% and \\ 97.62\% in plans with \\ simple funds and 85.73\% in complex funds \\ in complex funds \\ \end{tabular} } \\  & Automated People & \multirow{2}{*}{2023} & \multirow{2}{*}{MS-COCO} & \multirow{2}{*}{Mask R-CNN,} & \multirow{2}{*}{\
\begin{tabular}{c} mAP of 100\% and \\ 97.62\% in plans with \\ single funds and 85.73\% in complex funds \\ in complex funds \\ \end{tabular} } \\  & Counting System & \multirow{2}{*}{2023} & \multirow{2}{*}{MS-COCO} & \multirow{2}{*}{Mask R-CNN,} & \multirow{2}{*}{
\begin{tabular}{c} mAP of 100\% and \\ 97.62\% in plans with \\ single funds and 85.73\% in complex funds \\ in complex funds \\ \end{tabular} } \\  & using Object Detection & & & & \\ \multicolumn{4}{c}{and Tracking} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the articles studied. In [14], researchers in Vietnam studied a system for tracking vehicles on roads using a custom dataset and the YOLOv8s model. The custom dataset, containing images of Vietnamese traffic under various conditions, significantly outperformed a generic dataset (COCO) in terms of accuracy (82.9% vs. 15.6%). This suggests that YOLOv8 performs well on data similar to its training data but might be affected by factors like weather and camera angles. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Study** & **Year** & **Dataset** & **Methodologies** & **Results** \\ \hline \multirow{3}{*}{[16]} & Pedestrian and Object & \multirow{3}{*}{2023} & \multirow{3}{*}{MS-COCO} & YOLOv3 & mAP of 57.9\% \\  & Detection using Image & & & & \\ \cline{1-1}  & Processing by & & & & \\ YOLOv3 and YOLOv2 & & & & & \\ \hline \multirow{3}{*}{[17]} & Vehicle Counting & \multirow{3}{*}{2023} & \multirow{3}{*}{UA-DETRAC} & YOLOv3, SORT & Counting accuracy \\  & based on Convolution & NeuralNetwork & & & & of 85.45\% \\ \hline \multirow{3}{*}{[18]} & People Detecting and & \multirow{3}{*}{2021} & \multirow{3}{*}{ImageNet} & \multirow{3}{*}{ImageNet} & \multirow{3}{*}{N/A} \\  & Counting System & & & & \\ \hline \multirow{3}{*}{[19]} & Intelligent multimodal & \multirow{3}{*}{2023} & \multirow{3}{*}{UCSD (Ped-1 e & YOLOv5, & AUC score of 98.86\% \\  & pedestrian detection & & & & (Ped-1) and 97.58\% \\  & using hybrid & & & & (Ped-2) \\  & metaheuristic & & & & (Ped-2) \\  & optimization with & & & & \\  & deep learning model & & & & \\ \hline \multirow{3}{*}{[20]} & Realtime Vehicle & \multirow{3}{*}{2021} & \multirow{3}{*}{3 min video (origin & Haar Cascade & \multirow{3}{*}{mAP of 91.2\%} \\  & Counting Method & & not described) & & \\  & Using Haar Cascade & & & & \\  & Classifier Model & & & & \\ \hline \multirow{3}{*}{[7]} & Real-time Train Wagon & \multirow{3}{*}{2022} & \multirow{3}{*}{Dataset created by } & \multirow{3}{*}{ResNet-18, } & Accuracy of 99.2\% at 36 FPS (ResNet-18), & 36 FPS (ResNet-34), & 99.4\% at 22 FPS (ResNet-34), & \multirow{3}{*}{mAP of 91.2\%} \\  & Counting Algorithm & & &In [15], the paper describes an offline people-counting system using video files. It employs Mask R-CNN for detection, is trained on a large dataset (MS-COCO), and uses background subtraction, blob analysis, and centroid setting for counting.