Machine Learning Methods for Shark Detection
This work addresses the problem of improving shark detection for beach safety, but it appears incremental as it builds on existing methods without reporting concrete performance gains.
The paper reviewed human observer-based shark spotting methods at Muizenberg Beach and investigated machine learning approaches for automated shark detection to enhance human observation, with preliminary results indicating that useful information can be extracted from shark images despite geometric transformations.
This essay reviews human observer-based methods employed in shark spotting in Muizenberg Beach. It investigates Machine Learning methods for automated shark detection with the aim of enhancing human observation. A questionnaire and interview were used to collect information about shark spotting, the motivation of the actual Shark Spotter program and its limitations. We have defined a list of desirable properties for our model and chosen the adequate mathematical techniques. The preliminary results of the research show that we can expect to extract useful information from shark images despite the geometric transformations that sharks perform, its features do not change. To conclude, we have partially implemented our model; the remaining implementation requires dataset.