Keeping track of individuals in an endangered population of animals is a cumbersome and time-consuming task.
Conservationists physically tag animals in the wild to better follow them over time. But tagging can be intrusive for many species, and difficult to accomplish in larger populations. As an alternative, scientists have photographed animals in their natural environments and catalogued the images, along with information such as individuals’ dimensions and geographic locations.
However, as images accumulate, picking out individuals from among thousands of pictures can be a monumental task. Sai Ravela, a principal research scientist in MIT’s Department of Earth, Atmospheric and Planetary Sciences, estimates that manually sifting through a catalog of 10,000 images can take one person 15 years.
“It’s an enormous amount of time,” Ravela says. “You’re reaching the edges of what people want to do with their lives.”
Now Ravela and his colleagues at MIT have developed computer software that automates much of the image-matching process. The system, which they’ve named SLOOP, sifts through thousands of images, using pattern-recognition algorithms to analyze features in each image, such as an animal’s arrangement of stripes or spots. The system then identifies an average of 20 most likely matches for an individual.
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