- Title
- Computer vision as a tool for tracking gastropod chemical trails
- Creator
- Viviers, Andre
- Subject
- Computers
- Subject
- Electronic data processing
- Subject
- Machine learning
- Date Issued
- 2024-04
- Date
- 2024-04
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10948/64863
- Identifier
- vital:73934
- Description
- The difficulties encountered in previous gastropod research with human intervention (Raw, Miranda, & Perissinotto, 2013) inspired this dissertation. More specifically the tedious task of human intervention in the tracking of gastropod chemical trails, which is a time-consuming and error-prone exercise. In this study, computer vision is proposed as an alternative to human intervention. A machine learning literature review was conducted to identify relevant methodologies and techniques for the research. Furthermore, it investigates data preprocessing techniques on a variety of different data types. This sets the stage for a deeper investigation of techniques used for pre-processing image and video data. Following that, another literature review delved deeper into the computer vision pipeline. The review is divided into two parts: data pre-processing and model training. First, it provides a deeper investigation into relevant data pre-processing techniques for use in constructing a dataset comprised of gastropod images. Following that, it delves into the complexities of training a computer vision model. The study then investigates convolutional neural networks, revealing the neural networks’ suitability in image/video processing. A convolutional neural network is selected as the foundation for the best-effort model. This serves as the foundation for the subsequent experimental research. The first part of the experimental work involves creating a labelled dataset from the video dataset provided by Raw et al. (2013). By employing data preprocessing techniques in a strategic manner, an unlabeled dataset is generated. Then a labelled dataset is generated using a simple K-Means clustering algorithm and manual labelling. Thereafter, a best-effort model is trained to detect gastropods within images using this dataset. After making the labelled dataset, the next step in the exploration is to build a prototype that can find gastropods and draw trace lines based on their movement. Five evaluation runs serve to gauge the prototype’s effectiveness. Videos with varying properties from the original dataset are purposefully chosen for each run. The prototype’s trace lines are compared to the original dataset’s human-drawn pathways. The versatility of the prototype is demonstrated in the final evaluation by generating fine-grained trace lines post-processing. This enables the plot to be adjusted to different parameters based on the characteristics that the resulting plot should have. Through the versatility and accuracy demonstrated by the evaluation runs, this research found that a gastropod tracking solution based on computer vision can alleviate human intervention. The dissertation concludes with a discourse on the lessons learned from the research study. These are presented as guidelines to aid future work in developing a gastropod tracking solution based on computer vision.
- Description
- Thesis (MIT) -- Faculty of Engineering, the Built Environment, and Technology, School of Information Technology, 2024
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (viii, 129 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Engineering, the Built Environment, and Technology
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Viviers, A.pdf | 3 MB | Adobe Acrobat PDF | View Details Download |