New AI cameras draw conclusions about what they see at a speed of 8200 frames per second

Today, modern systems perceive and process the world around themThey're not good enough.

As a result, AI systems perceive the world only after recording and transmitting visual information between sensors and processors.But many of the things that cameras can see often don'tFor example, detailing the leaves on the trees by the road on which he is drivingNow all this unnecessary information is recorded by sensors with the smallest details and clogs upA different approach is needed to ensure the effective vision of intelligent machines.

Two studies, from Bristol and Manchester, have shown how perception and learning can be combined to create new camerasThey presented a convolutional neural network (CNN) in the SCAMP-5D vision system.It is able to classify what is happening in front of it at a speed of 8,200 frames per second. 

A convolutional neural network (CNN) in the SCAMP-5D computer vision system that classifies hand gestures at 8,200 frames per second. Credit: University of Bristol

Recognize images without the need to record them or send them further to processing systems. 

SCAMP-5d technical vision system. Credit: University of Manchester

This approach will make the systems much more efficient and safer as there is no need to record images to analyze them.

Work made possible by architectureSCAMP, developed by Peter Dudek, professor at the University of Manchester, and his team. SCAMP is a camera processor chip, which scientists describe as a Pixel Processor Array (PPA). The PPA has a processor built into each pixel that can communicate with each other to process data in parallel. This is perfect for CNN and vision algorithms.

The development of scientists is presented at the European Conference on Computer Vision (ECCV).

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