Counting animals in ecological images

dc.contributor.advisorDurbach, Ian
dc.contributor.advisorDufourq, Emmanuel
dc.contributor.authorPillay, Nakkita
dc.date.accessioned2022-06-24T10:13:27Z
dc.date.available2022-06-24T10:13:27Z
dc.date.issued2022
dc.date.updated2022-06-24T09:54:34Z
dc.description.abstractIn 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.apacitationPillay, N. (2022). <i>Counting animals in ecological images</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/36534en_ZA
dc.identifier.chicagocitationPillay, Nakkita. <i>"Counting animals in ecological images."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2022. http://hdl.handle.net/11427/36534en_ZA
dc.identifier.citationPillay, N. 2022. Counting animals in ecological images. . ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/36534en_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.urihttp://hdl.handle.net/11427/36534
dc.identifier.vancouvercitationPillay N. Counting animals in ecological images. []. ,Faculty of Science ,Department of Statistical Sciences, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/36534en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Statistical Sciences
dc.publisher.facultyFaculty of Science
dc.subjectStatistical Sciences
dc.titleCounting animals in ecological images
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
thesis_sci_2022_pillay nakkita.pdf
Size:
3.39 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
0 B
Format:
Item-specific license agreed upon to submission
Description:
Collections