# Using Computer Vision to Collect Information on Cycling and Hiking Trails Users
## 2 Related Work
### Analysis of Studies
Additionally, it integrates a notification system through ThingSpeak and an old social network (potentially Twitter) to display the count. The system achieved high accuracy (100% and 97.62%) in simple test scenarios and a lower, but still reasonable, accuracy (85.73%) in a more complex scenario. In [16], this study proposes a real-time object detection system for vehicles, aiming to enhance road safety by identifying pedestrians and other objects and notifying drivers of potential hazards. Utilizing the COCO dataset, the system compares the performance of YOLOv2 and YOLOv3 algorithms. The findings reveal that YOLOv3 achieves superior accuracy (approximately 58% mAP) and operates at a faster refresh rate, rendering it more suitable for real-time applications. Conversely, YOLOv2 exhibits limitations in both object detection and precision, hindering its effectiveness in this context. In [17], this study presents a method for vehicle detection, classification, and counting using a Virtual Detection Zone (VDZ) aimed at simplifying traffic management applications. The UA-DETRAC dataset was used for evaluation. The system leverages OpenCV, the YOLOv3 model (with Darknet-53 as the feature extractor), and the SORT algorithm for tracking. While minor challenges with duplicate counting were observed in specific scenarios, the system demonstrated a high overall accuracy of approximately 85.45%. In [18], a people counting system for monitoring entry and exit points is presented in this study. The system leverages a Raspberry Pi for image capture, model execution, and data transmission to a web server. Trained on the ImageNet dataset, the model employs Support Vector Machines alongside HOG and VGG-16 models to classify objects  
Figure 3: Flowchart of the research phases. as \"person\" or \"non-person\". Both models exhibit comparable accuracy, although HOG extracts a substantially higher number of features compared to VGG-16. In [19], a multimodal pedestrian detection and classification system, IPDC-HMODL, is introduced in this work. This deep learning-based model, employing techniques like hybrid metaheuristic optimization and kernel extreme learning machines, demonstrates superior performance on the UCSD Ped-1 and Ped-2 datasets compared to existing methods. IPDC-HMODL achieves exceptional Area Under the Curve (AUC) scores (98.86% and 97.58%) and low error rates (3.12% and 4.35%) on both datasets, surpassing established models like AMDN, MDT, and Social Force. Receiver Operating Characteristic (ROC) analysis further emphasizes IPDC-HMODL's efficacy, highlighting its potential for real-world applications in pedestrian detection and classification tasks. In [20], a method was described for the real-time counting of vehicles through the application of computer vision in conjunction with a Single-board Computer (SBC). The main objective was to present an efficient system capable of identifying and counting vehicles entering and leaving certain entrances. The dataset used for evaluation was not explicitly detailed. The hardware used consisted of a Raspberry Pi equipped with a camera module. The software used was the Haar Cascade Classifier algorithm, implemented in the Python programming language. The test results showed that the proposed system achieved an average accuracy of 91.2% in detecting and accurately counting moving vehicles. In [7], a solution was proposed for train carriage counting and number recognition using deep learning computer vision models. The system processes a video stream into frames and uses three datasets: \"Scene Classifier\" with 7000 backgrounds, 12,000 intervals, and 11,000 wagon images to train the classifier; \"Number detection model\" with 3400 wagon images for detecting number edges; and \"digit recognition model\" with 3400 images for recognizing digits. The first phase validated wagon counting with 99.5% accuracy using ResNet models, while the second phase achieved 98.5% accuracy in recognizing wagon numbers. This approach relies solely on computer vision without additional devices or sensors.