This repository holds the source files used to generate my 2015 Master's Thesis entitled, “Photographic Censusing of Zebra and Giraffe in the Nairobi National Park”.
This thesis document is meant to be a partial fullfillment of the requirements for a M.S. degree in Computer Science at RPI. The thesis is paired with an oral presentation (given in August 2015) and an accompanying poster. The final draft was submitted to the University in November 2015 for graduation December, 2015.
Master's Committee
- Charles Stewart, Thesis Advisor, Computer Science Department Chair
- Barbara Cutler
- Bülent Yener
The final thesis document can be viewed in rpithesis.pdf
Note: From feedback from the committee after the oral presentation, the title of the thesis was changed from “How Many Plains Zebras And Masai Giraffes are in the Nairobi National Park? - A Case Study in Computer Vision and Citizen Science” to “Photographic Censusing of Zebra and Giraffe in the Nairobi National Park”
Establishing a population estimate for the plains zebras and Masai giraffes in the Nairobi National Park (NNP) can be a challenging task due to the large size of the conservation area and large population. Making the situation worse, the NNP is not fenced on its southern boundary, which makes traditional counting methods impractical and unpredictable due to the park's arbitrary population. Traditional and invasive identification methods (e.g. ear tags, ear notches, radio collars) are costly and infeasible for large populations. As an alternative, we propose a passive, appearance-based approach that uses images of animals taken by volunteer “citizen scientists” to identify individuals. Image data is analyzed using our prototype IBEIS computer vision algorithm, which recognizes animals based solely on their appearance. The collection of images over time allows for a more comprehensive ecological analysis of the animal population and the park's ecosystem. By providing actionable ecological data, our method allows the conservationists in the NNP to make data-driven decisions in order to accomplish their conservation goals.
In March of 2015, the IBEIS team helped to administer The Great Zebra & Giraffe Count (GZGC), which collected 9,406 images from 58 volunteer citizen scientists. The contributed images yielded a total of 8,659 sightings of plains zebras (Equus quagga), of which we identified 1,258 individuals. We performed a photographic censusing, or photographic mark-recapture (PMR) study, on the last two days of data collection and, using the Lincoln-Peterson method, estimated a total of 2,307 ± 366 zebras in the NNP (confidence of 95%). The IBEIS team also analyzed images of Masai giraffes (Giraffa camelopardalis tippelskirchi), which yielded a total of 1,258 sightings. Of the sighted giraffes, we found 103 individuals and calculated a Peterson-Lincoln Index of 119 ± 48.
To our knowledge, this is the first time a population estimate of the plains zebras and Masai giraffes has ever been performed using an automated appearance- based approach. We also believe that the GZGC was the largest citizen science data collection event ever performed inside the park, to date.
- https://github.com/Erotemic/ibeis
- https://github.com/bluemellophone/gzc-client
- https://github.com/bluemellophone/gzc-server
- Install LaTeX and BibTeX
- Build rpithesis.tex
- View PDF document in rpithesis.pdf
- Computer Operating Systems [3]
- Cryptography & Network Security I [3]
- Randomized Algorithms [3]
- Cryptography & Network Security II [3]
- Machine Learning [3]
- Programming Languages [3]
- Computational Vision [3]
- Neural Networks for Computer Vision [3]
- Master's Thesis Credits (Dr. Stewart) [6]
- Computer Science
- Ecology
- photographic censusing
- Kenya
- IBEIS
- Great Zebra & Giraffe Count
- citizen science
- computer vision
https://www.zotero.org/groups/chucks_rpi_vision_group/items/collectionKey/7K23MMC8
The Great Zebra & Giraffe Count (GZGC) was powered and administered by the IBEIS team with notable help from Dr. Paula Kahumbu and staff at Wildlife Direct in Nairobi, Kenya. The IBEIS team would like to thank the people and government of Kenya for supporting this research (Permit # NACOSTI/P/14/1003/1628), with special recognition to Senior Warden of the Nairobi National Park, Nely Palmeris, and Macharia “Michael” Kimura of the Kenyan Wildlife Service (KWS). Other contributions to this thesis were provided by Clara Machogu, Marco Maggioni, Jon Crall, Hendrik Weideman, Michael Brown, and Zachary Jablons. I would like to thank Dr. Tanya Berger-Wolf, Dr. Daniel Rubenstein, and my master committee members, Dr. Barbara Cutler and Dr. Bülent Yener, for their valuable feedback. I would also like to give a special thank you to my Ph.D. advisor, Dr. Charles Stewart, who has patiently guided me to this milestone of my academic career.
The open-source software and research detailed in this thesis was supported by Rensselaer Polytechnic Institute (RPI) and with financial support from NSF EAGER Grant (Award # 1453503) Collaborative Research: EAGER: Prototype of an Image-Based Ecological Information System (IBEIS).
Lastly, but most importantly, I would like to thank my wife, Lindsay, for providing patient, never-failing support. Her dedication to my academic career and intellectual progress has been the best example of sacrificial love I have ever had the privilege to personally experience.
