Counting animals in ecological images
| dc.contributor.advisor | Durbach, Ian | |
| dc.contributor.advisor | Dufourq, Emmanuel | |
| dc.contributor.author | Pillay, Nakkita | |
| dc.date.accessioned | 2022-06-24T10:13:27Z | |
| dc.date.available | 2022-06-24T10:13:27Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2022-06-24T09:54:34Z | |
| dc.description.abstract | In the field of ecology, counting of animals to estimate population size and prey abundance is important for the conservation of wildlife. This involves analyzing large volumes of image, video or audio data and manual counting. Automating the process of counting animals would be invaluable to researchers as it will eliminate the tedious time-consuming task of counting. The purpose of this dissertation is to address manual counting in images by implementing an automated solution using computer vision. This research applies a blob detection algorithm primarily based on the determinant of the Hessian matrix to estimate counts of animals in aerial images of colonies in a user-friendly web application and trains an object detection model using deep convolutional neural networks to automatically identify and count penguin prey in 2053 images extracted from animal-borne videos. The blob detection algorithm reports an average relative bias of less than 6% and the YOLOv3 object detection model automatically detects jellyfish, school of fish and fish with a mean average precision of 82,53% and counts with an average relative bias of -17,66% over all classes. The results show that applying traditional computer vision methods and deep learning on data-scarce and data-rich situations respectively, can save ecologists an immense amount of time used on manual tedious methods of analysis and counting. Additionally, these automated counting methods can contribute towards improving wildlife conservation and future studies. | |
| dc.identifier.apacitation | Pillay, N. (2022). <i>Counting animals in ecological images</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/36534 | en_ZA |
| dc.identifier.chicagocitation | Pillay, Nakkita. <i>"Counting animals in ecological images."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2022. http://hdl.handle.net/11427/36534 | en_ZA |
| dc.identifier.citation | Pillay, N. 2022. Counting animals in ecological images. . ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/36534 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Pillay, Nakkita AB - In the field of ecology, counting of animals to estimate population size and prey abundance is important for the conservation of wildlife. This involves analyzing large volumes of image, video or audio data and manual counting. Automating the process of counting animals would be invaluable to researchers as it will eliminate the tedious time-consuming task of counting. The purpose of this dissertation is to address manual counting in images by implementing an automated solution using computer vision. This research applies a blob detection algorithm primarily based on the determinant of the Hessian matrix to estimate counts of animals in aerial images of colonies in a user-friendly web application and trains an object detection model using deep convolutional neural networks to automatically identify and count penguin prey in 2053 images extracted from animal-borne videos. The blob detection algorithm reports an average relative bias of less than 6% and the YOLOv3 object detection model automatically detects jellyfish, school of fish and fish with a mean average precision of 82,53% and counts with an average relative bias of -17,66% over all classes. The results show that applying traditional computer vision methods and deep learning on data-scarce and data-rich situations respectively, can save ecologists an immense amount of time used on manual tedious methods of analysis and counting. Additionally, these automated counting methods can contribute towards improving wildlife conservation and future studies. DA - 2022 DB - OpenUCT DP - University of Cape Town KW - Statistical Sciences LK - https://open.uct.ac.za PY - 2022 T1 - Counting animals in ecological images TI - Counting animals in ecological images UR - http://hdl.handle.net/11427/36534 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/36534 | |
| dc.identifier.vancouvercitation | Pillay N. Counting animals in ecological images. []. ,Faculty of Science ,Department of Statistical Sciences, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/36534 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Statistical Sciences | |
| dc.title | Counting animals in ecological images | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |