A new occupancy index model based on artificial vision for enhancing beach management

This study proposes a new method to more effectively plan the use of beaches by combining indices and artificial vision systems. The Overcrowding Index (I_ocr) measures the number of people on the beach in relation to its surface area, while the Distancing Index (I_dis) evaluates the spatial distribution and distance between beachgoers. Both indices are combined to generate an overall index called the Occupancy Index (I_ocu). The proposed methodology uses cameras and computer vision algorithms such as YOLOX and ByteTrack to automate the counting of people and measure distances. This allows for continuous monitoring of the quantity (carrying capacity and density) and distribution of beachgoers (degree of social distancing), as well as a functional prototype in which the indices are calculated in real time. It was observed that as density increased, Iocr showed an inverse trend, being close to 0 when approaching maximum density. The calculation of the distance between groups validated that, even with medium densities, close to the shoreline, the reference distance of 2 m was not accomplished, obtaining a very low I_dis (0.18). The resulting I_ocu was 0.31, validating the appropriate integration of both indices. Overall, the system's effectiveness for accurately monitoring the number of users and their distribution, and calculating the defined indices for beach management, is demonstrated. The proposed approach provides a valuable tool, allowing a more efficient management of beaches according to their actual occupancy and user distribution.