A major concern shared by both the US and its allies like South Korea is that the growing ubiquity of low-cost UAVs allows anyone with easy access to the simplest of these devices to enter regions of either civil or military interest and wreak havoc in ways unimaginable. A member of our diverse team is an on-duty officer of the South Korean Air Force and is intimately familiar with swarms of COTS sUAS administered by the North Korean military being used for reconnaissance and/or surveillance across the border adjoining the two nations. We believe it is of prime importance for agencies engaged in national security to be, at the very least, capable of detecting and tracking these devices so as to better protect the interests of those they serve.

What it does

Our solution uses a ground network of cameras which monitor the airspace to detect a sUAV using computer vision and machine learning algorithms. When a foreign sUAV is detected, an anti-UAV is launched which uses an onboard camera, filtering and estimation techniques to track and follow the enemy sUAV. Ability to accurately detect, estimate, and track objects in the field of vision has huge implications for the private industry - especially those that employ pattern/feature recognition in their services.

How I built it

We created a local WiFi network using ROS, through which cameras and computers who process images communicate. Machine learning and motion detection algorithms are used to identify the moving target. RGB cameras are used for stereoscopic computer vision which triangulates the position of the target in 3D space. Then the fixed target is tracked using a drone with onboard camera.

Challenges I ran into

Needed to simplify estimation objective. ROS troubles dampened elimination efforts.

Accomplishments that I'm proud of

In spite of technical glitches, constrained time and limited resources, we were able to demo our prototype for target detection and triangulation using stereoscopic cameras.

What I learned

Refocused on the service/product rather than the results of hackathon prototyping.

What's next for The "A" Team

Complete development of the proposed product. Focus on the modularity of the overall solution. Make overall algorithms compatible with different types of cameras like IR/night vision cameras.

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