AI News, AI computer vision breakthrough IDs poachers in less than half a second

AI computer vision breakthrough IDs poachers in less than half a second

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.

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.

'SPOT will ease the burden on those using drones for anti-poaching by automatically detecting people and animals in infrared imagery, and by providing detections in near real time,' says lead author Elizabeth Bondi, a PhD candidate in computer science at USC.

AI system developed to instantly identify and catch animal poachers

Thousands of animals including elephants, tigers, rhinos, and gorillas are currently poached each year and many of the perpetrators are never caught.

While tools such as infrared cameras are used to monitor living organisms, since both the poachers and the animals they are hunting give off heat, it is time-consuming and challenging to monitor infrared video streams for poachers all night.

The researchers used these labelled images and leveraged an existing deep-learning algorithm known as Faster RCNN, which they modified to teach a computer to automatically distinguish infrared images of humans from 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.

AI computer vision breakthrough IDs poachers in less than half a second

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.

The algorithm, while functioning with accuracy, was taking 10 seconds to process each image —which is too long for the moving vehicles.

The researchers then changed the algorithm to work with Microsoft Azure—-leveraging the power of the cloud to build a virtual computer that could do faster processing.

How Do You Count Endangered Species? Look to the Stars

On a sunny, summer day in 2015, the team flew their drones over a farm to see if their machine-learning algorithms could locate the animals in infrared footage.

But accuracy was compromised when drones flew too high, cows huddled together, or roads and rocks heated up in the sun.

In a later test, the machines occasionally mistook hot rocks for students pretending to be poachers hiding in the bush.

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