Poachers are normally active at night. While tools such as infrared cameras are used to monitor living organisms, since poachers and animals they are hunting both give off heat, it is time-consuming and challenging to monitor infrared video streams for poachers all night. Thus a team of computer scientists led by USC Viterbi School of Engineering PhD student Elizabeth Bondi in Professor Milind Tambe's lab, labeled 180,000 humans and animals in infrared videos using a labeling tool they developed to expedite the process. The researchers used these labeled images and leveraged an existing deep learning algorithm known as Faster RCNN that they modified, to teach a computer to automatically distinguish infrared images of humans from those infrared images of animals.
The challenge then was to deploy this algorithm to spot poachers in near real time using the laptop computers at base stations in the field, where footage is streamed from the drones that are being used to patrol national parks in Zimbabwe and Malawi....
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