# Machine Learning Methods for Shark Detection

## TL;DR

This paper explores machine learning techniques to automate shark detection from images, aiming to improve upon human observation methods used at Muizenberg Beach, with preliminary promising results.

## Contribution

It defines desirable properties for shark detection models and presents a partially implemented machine learning approach tailored to shark image analysis.

## Key findings

- Shark features remain consistent despite geometric transformations.
- Preliminary results indicate potential for useful information extraction.
- The model's full implementation depends on dataset availability.

## Abstract

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.