# Using Computer Vision to Collect Information on Cycling and Hiking Trails Users
## 1 Introduction  
In recent years, walking, hiking, and cycling have become one of the most popular activities for promoting physical activity and well-being. These are carried out through trails or routes of varying lengths, classified into three categories depending on the distances involved: Long Routes (GR), routes over 30 km long; Short Routes (PR), less than 30 km long; and Local Routes (PL), where all or more than half of the route is in an urban environment [1]. Each of these trails is duly marked with the signs displayed in Figure 1 along the way. These markings can appear in a wide variety of places, such as rocks, walls, stakes, and electricity pylons, among others [2]. Figure 2 shows some examples of the location of these marks. Walking, hiking, and cycling trails and routes are created and maintained by various entities, such as federations related to sports, mountaineering, camping, national, regional, or local public administration entities, and even entities linked to the tourism sector [3]. According to the Federation of Camping and Mountaineering of Portugal (FCM) [4], the body responsible for registering and homologating hiking trails, there are 76 small routes and 46 large routes homological in Portugal, following the Regulation for the Homologation of Walking Routes (RHPP) [1]. This regulation serves to establish specific standards and criteria for a hiking trail to be officially recognized [5]. However, there are many routes  
In Portugal, the number of approved walking, hiking, and cycling trails and routes has been growing over the years, helping to add value to areas by attracting a growing number of domestic and foreign tourists to practice nature sports and boosting sustainable tourism. However, since these spaces are freely accessible, there is no reliable information available on how often they are used and the types of users that use them most often. Given that, in most cases, municipalities are the driving force behind these infrastructures, it is important to assess, for example, the real tourist impact that these routes bring to the regions. Accurate and reliable data are therefore needed to show how they are used. In this way, identifying the different types of users, counting them, and knowing the times of greatest influx are crucial data for the managers and promoters of these infrastructures. These data can also help gauge the strategic importance of these facilities in tourism development, especially in low-density areas. Another dimension that can be considered when analyzing the data collected is the management of the capacity to use these infrastructures. Given that, along these trails, there are often sensitive areas that have specific preservation regulations (such as natural parks, protected areas, and special protection zones, among others), the collection of data on usage rates is extremely important for managing access to these areas. Considering the importance of these data in better managing these infrastructures, there is a need to implement solutions that allow these data to be collected, processed, and analyzed. The work presented in this paper represents a first step in an ongoing effort to propose, develop, and evaluate a solution that allows for identifying and quantifying different types of users on walking, hiking, and cycling trails or routes. To achieve this goal, and after a preliminary analysis, it was decided to study solutions that use computer vision techniques, since, as demonstrated in [6; 7], these stand out as the main technique for detecting moving objects. Moreover, computer vision demonstrates greater versatility and adaptability to various conditions compared to other technologies like LiDAR [8]. Opting for its use not only contributes to the evolution of this field but also ensures the availability of more data for future utilization [9].