Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video

Automatically creating spatio-temporal occupancy analysis of public swimming pools is of great interest, both for administrators to optimize the use of these expensive facilities, and for users to schedule their activities outside peak hours. In this paper we apply current state-of-the-art deep learning methods within human detection on low quality swimming pool video. Furthermore, we propose a method for analyzing the spatio-temporal occupancy of a swimming pool. We show that it is possible to precisely detect swimmers in very challenging conditions by obtaining an AUC of 93.48 % from YOLOv2. An acceptable AUC of 79.29 % was obtained from Tiny-YOLO, which can be implemented on a low-cost embedded system capable of producing results in real-time on site. We expect that the performance of both networks can be improved with more training data.