If you watch animal documentaries or TV shows on Animal Planet, you may be familiar with how scientists use physical tags to track animals in the wild. However, sometimes tagging can be difficult or intrusive on an animal’s environment. A recent article from LaboratoryEquipment.com highlights how scientists at MIT are using photographs to track animals, along with algorithmic pattern recognition and Mechanical Turk Workers to identify animals in the images collected.
While capturing photographs is less intrusive, reviewing the shots collected is an arduous task. To keep scientists in the field (as opposed to manually reviewing images in front of a computer), Ravela’s team developed a software system that uses algorithmic pattern recognition to identify animals based on their unique features, such as stripes or spots.
Ravela’s team was able to successfully identify potential image matches algorithmically, so the team turned to Mechanical Turk to create a scalable process for manual verification. To identify the most qualified Workers for the task, each Worker was presented with four images, where the correct answer was known for three. If the Worker correctly matched the known images, then the system would automatically trust the accuracy fourth result.
Read about MIT’s experiment at on LaboratoryEquipment.com. To learn more about how you can use Known Answers to monitor performance, see our previous post on the topic or read more in our Mechanical Turk’s API documentation.