Saliency based methods for camera orientation in aquaculture

Abstract

This thesis looks into the applicability of visual saliency as a basis for autonomous camera orientation. Autonomous re-orientation is expected to be a key component in future computer vision-based monitoring systems: a fish-cage is a highly dynamic environment, and even a well-placed camera may no longer be optimally oriented for data gathering if left static over time. The underwater performance of visual saliency algorithms are tested and discussed. A few different attempts at addressing a major concern related to the cage net itself are made. Most notable is a filtering scheme based on an optical flow algorithm by Gunnar Farnebäck, the use of which successfully solves the presented challenge but also introduces a new problem. In conclusion, visual saliency can provide a viable basis for camera orientation, but only under certain conditions.