Publication Date

Fall 2021

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Robert Chun

Second Advisor

William Andreopoulos

Third Advisor

Kun Liu


Image Classification, Machine Learning, Convolutional Neural Networks, Detectron2, Computer Vision, Camera Traps


Motion-sensitive cameras, otherwise known as camera traps, have become increasingly popular amongst ecologists for studying wildlife. These cameras allow scientists to remotely observe animals through an inexpensive and non-invasive approach. Due to the lenient nature of motion cameras, studies involving them often generate excessive amounts of footage with many photographs not containing any animal subjects. Thus, there is a need for a system that is capable of analyzing camera trap footage to determine if a picture holds value for researchers. While research into automated image recognition is well documented, it has had limited applications in the field of ecology. This thesis will investigate previous approaches used for analyzing camera trap footage. Studies involving traditional computer vision and machine learning techniques are reviewed. Furthermore, the datasets and additional feature recognition utilized by the techniques will be explored to showcase the advantages and disadvantages of each process, and to determine if it is possible to improve upon them.