Undergraduate Research: An Archive - 2021 Program

Pablo Bickenbach ’21
COMPUTER SCIENCE B.S.E.
Certificate in Environmental Studies
BIODIVERSITY AND
CONSERVATION
THESIS TITLE
Computer Vision for
Wildlife Conservation: A
Detection and
Classification Pipeline
for Camera Trap Images
ADVISER
Olga Russakovsky,
Assistant Professor of
Computer Science
I applied computer-vision techniques to address
a real-world problem in wildlife conservation
—the filtering and classification of large
numbers of images captured with motionsensor
camera traps. Through the use of deep
learning convolutional neural networks (CNNs),
I built a pipeline that automates this task, first
by detecting the presence of animals in camera
trap images (and discarding “empty” images),
then by classifying these detections by animal
species. When tested on data sets from different
African nature reserves, the pipeline achieved an
overall accuracy of 75% to 88%, demonstrating its
efficacy and its potential for aiding camera trap
conservation projects.
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