Imagine a downtown urban street on a traffic-heavy Saturday night.
Now, add a severe snowstorm and an autonomous vehicle that safely navigates it all, despite
fogged-up cameras,
unpredictable pedestrians in oversized outerwear,
streetlights throwing glare off a dozen wet surfaces, and
the snow itself, which all but obliterates the visibility of traffic signs and lane markers.
A computer vision (CV) system that could make this safe navigation possible would revolutionize not only autonomous vehicles but also robotics, security, and other domains.
Moonshot goals like this eventually become reality through events like the Low-Power-Computer Vision Challenge (LPCVC), which just wrapped up in Nashville at the IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR).
LPCVC founder and organizer Yung-Hsiang Lu, a professor of electrical and computer engineering at Purdue University, said the month-long challenge–which ran from 1–31 March–offers many benefits, both in itself and for the future.
“Participating in LPCVC gives [people] an opportunity to compete with the world’s top researchers,” said Lu. “For the field, LPCVC gives researchers from different organizations and countries a chance to share their best solutions. Together, they propel the technology forward.”
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