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Technology Innovation Institute Launches the TII Infrared Tracking Challenge to Usher in New Era of Autonomous Vehicles

Technology Innovation Institute (TII), a leading global scientific research center and applied research pillar of Abu Dhabi’s Advanced Technology Research Council (ATRC), and HeroX, the leading platform and open marketplace for crowdsourced solutions, launched the crowdsourcing competition, the TII Infrared Tracking Challenge. The Challenge seeks approaches for effectively tracking objects in low-light conditions, both in structured and unstructured environments.

New generation autonomous vehicles must successfully navigate environments with challenging lighting conditions, and current perception-based tracking algorithms often fall short. TII is, therefore, exploring the use of radiometric sensors, which can provide infrared, rather than photometric frames for object tracking in low-light conditions.

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“Autonomous vehicles are the future,” said Dr. Najwa Aaraj, Chief Researcher, Cryptography Research Center, and Chief Researcher, Autonomous Robotics Research Center, at the Technology Innovation Institute. “While autonomous driving technology still has some ways to go in achieving operational and technical impeccability, this Challenge will help innovators find solutions to ensure that autonomous vehicles on our roads operate as safely as possible. Such advancements will launch us into a bold, new phase of our autonomous transportation journey, and we are eager to see what the crowd comes up with.”

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“You can always count on our network of innovators to push the envelope and propel us in exciting new directions,” said Kal K. Sahota, CEO, HeroX. “Enhancing autonomous vehicles is no exception: tapping the HeroX crowd is a strategic and efficient way to source innovations that will bring us into a thrilling new era for autonomous vehicles.”

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The Challenge:The TII Infrared Tracking Challenge is open to innovators, start-ups, research institutes, and university students from anywhere in the world. Challengers are encouraged to leverage publicly available datasets with similar content as TII’s provided sample dataset and utilize color-to-thermal domain adaptation techniques when necessary. Submissions will be evaluated based on TII-defined criteria.

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