Author(s)

Maria da Conceição Proença

Marine and Environmental Sciences Centre & ARNET, Aquatic Research Infrastructure Network Associated Laboratory, Department of Physics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.

This work focuses on the problem of monitoring the coastline, which in Portugal’s case means monitoring 3007 kilometers, including 1793 maritime borders with the Atlantic Ocean to the south and west. The human burden on the coast becomes a problem, both because erosion makes the cliffs unstable and because pollution increases, making the fragile dune ecosystem difficult to preserve. It is becoming necessary to increase the control of access to beaches, even if it is not a popular measure for internal and external tourism. The methodology described can also be used to monitor maritime borders. The use of images acquired in the infrared range guarantees active surveillance both day and night, the main objective being to mimic the infrared cameras already installed in some critical areas along the coastline. Using a series of infrared photographs taken at low angles with a modified camera and appropriate filter, a recent deep learning algorithm with the right training can simultaneously detect and count whole people at close range and people almost completely submerged in the water, including partially visible targets, achieving a performance with F1 score of 0.945, with 97% of targets correctly identified. This implementation is possible with ordinary laptop computers and could contribute to more frequent and more extensive coverage in beach/border surveillance, using infrared cameras at regular intervals. It can be partially automated to send alerts to the authorities and/or the nearest lifeguards, thus increasing monitoring without relying on human resources.

Beach Overload, People Counting, Border Control, People Detection, Deep Learning Methods, Remote Surveillance

Proença, M. (2024) Efficient People Detection with Infrared Images. Journal of Computer and Communications, 12, 31-39. doi: 10.4236/jcc.2024.124003.