Green, S., Blumenstein, M., Browne, M., & Tomlinson, R. (2005). The Detection and Quantification of Persons in Cluttered Beach Scenes Using Neural Network-Based Classification. In Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA’05) (pp. v-x). IEEE.
https://ieeexplore.ieee.org/document/1540692

has been cited by the following article:

• TITLE: Beach Surveillance: A Contribution to Automation

  AUTHORS: Maria da Conceição Proença, Ricardo Nogueira Mendes

  KEYWORDS: People Counting, Beach Surcharge, Human Detectors, Deep Learning Methodologies

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.12 No.12, December 25, 2024

  ABSTRACT: The problem of human overload in many habitats is becoming increasingly urgent, as it is the driving force that destroys ecosystems beyond repair. This paper describes a possible workflow for beach surveillance, using a deep learning solution available online that runs on a standard laptop with RGB images acquired with a standard camera. The software is YOLO v7, a state-of-the-art real-time object detection model presently used for autonomous driving, surveillance, and robotics. The workflow and parametrization needed for building a model are described, along with examples of the results over 180 test images that ensures an overall precision of 0.98 and recall of 0.94 (F1 = 0.96). The model was parametrized to focus on a minimum number of false positives; from the 5672 possible detections identified by human curation, 5285 were correctly identified and located, 387 missed and there are 116 mistakes. A minimum of computational skills is needed to reproduce this implementation in any user data of the same kind.