Navigational system for visually impaired people in a swimming pool

Sammanfattning: In this thesis conducted at IBM in Amsterdam we explore the ability Computer Vision has to assist visually impaired people in navigating a swimming pool. We examine different Computer Vision techniques and develop an algorithm to navigate a swimmer in a pool. In cooperation with a center for visually impaired people we collect a video-dataset that reflects the use-case at hand for testing, and to be able to utilize data-driven algorithms. The Computer Vision algorithm designed was implemented using Deep Learning (CNN) and statistical methods like Kalman filtering. Evaluation of the algorithm was done using both the dataset and by comparing the algorithm to the state of the art in pedestrian tracking using the MOT benchmark. The MOT benchmark was used in lack of standardized tests for tracking in pools, it provided an outlook of the algorithm’s performance in comparison to other methods. The results showed that the tracker could compete with the state of the art in pedestrian tracking as well as navigate swimmers in a pool. While the dataset needs to be expanded to perfect the algorithm, the thesis concludes that data-driven Computer Vision techniques can in a robust way navigate a swimmer in a pool with the help of statistical filtering. This is an important step to make visually impaired people more autonomous and in consequence healthier.