# Using Computer Vision to Collect Information on Cycling and Hiking Trails Users
## 2 Related Work
### Critical Analysis  
Throughout this study, several limitations were noted in the works analyzed, such as the absence of methodologies and datasets used to implement the solutions. In addition, some results are not explicitly presented. However, it was concluded that there are similarities in implementation between studies. After analyzing all the articles obtained, only 12 of them fall within the scope of this work. It should be noted that the choice of the CNN model to be used for detecting users of cycling and hiking trails and routes is very important. Not all detection models are equally efficient in terms of resource consumption, making it important to select based on the implementation environment. In the scenario of this work, where the system will be implemented in a remote location, it is anticipated that the hardware used may present computational limitations. It is therefore necessary to strike a balance between the accuracy of the model and the hardware's ability to respond to that model. Based on the analysis of the related studies, three models were identified that could potentially have the best results in the development of the solution to be proposed, for the reasons described below. YOLOv3 was the most widely used model in the studies analyzed and is therefore included in this selection. In the future, its Tiny version will be used, which is recognized for being lighter, more efficient, and less computationally demanding. The choice of the MobileNet-SSD model was based on its use in embedded boards with few computing resources. This selection was based on the model's ability to offer satisfactory performance (87.5% mAP [19]). Finally, the ResNet-50 model was also selected due to its remarkable accuracy performance in [7; 15], oscillating between 85% and 100% in various test conditions. It is also highlighted as the second most used model in the related works studied, demonstrating the relevance and efficiency of this model in different contexts. Considering that the system will be placed in remote locations (such as pedestrian routes), access to operational resources will probably be limited. Therefore, the option for a system with low power consumption should be considered. The adaptation of the dataset to the context is also a point of analysis. In the existing studies, no dataset has been identified that is perfectly aligned with the requirements of the work described in this paper. It is therefore essential to build one that encompasses people and bicycles and considers the possibility of including motorcycles. It is also important that the images that make up the dataset are captured on footpaths or in similar scenarios, such as dirt tracks. In this way, the development of the dataset plays a key role in training and testing the model to achieve more accurate detection. Finally, based on the analysis of the previous studies, it is now possible to answer the research questions. **Question No. 1--How can different types of users (cyclists and pedestrians) on cycling and hiking trails and routes be distinguished? **  
The classification of different types of users on cycling and pedestrian routes can be carried out using computer vision and machine learning (ML) techniques. A computer vision system can be trained to recognize distinctive features between cyclists and pedestrians, using convolutional neural networks to learn patterns in the data received from real-time video frames, thus distinguishing pedestrians from cyclists. All the articles studied present a detection model based on CNNs. **Question No. 2--How can different types of users (cyclists and pedestrians) on cycling and hiking trails and routes be counted? **  
Quantifying different types of users on cycling and pedestrian routes involves counting the number of cyclists and pedestrians present in a scene, which must first be detected. Object detection techniques can therefore be used to identify and locate cyclists and pedestrians in a video frame in real time. Counting is then carried out based on the detections.