LAS VEGAS — While GoPro suffers from the market saturation of mobile action cameras for sports enthusiasts, Ambarella, a very high-resolution image processor company that once generated as much as 30 percent of its revenue from GoPro, showcased at the Consumer Electronics Show its newly architected computer vision chip, CV1, primarily designed for highly automated vehicles.
On the eve of CES, GoPro announced plans to exit the drone business, cut 250 jobs and lower its fourth quarter revenue estimate. Fermi Wang, told EE Times that Ambarella has already experienced a decline in GoPro-based revenue. Last year, it was "10-plus percent," Wang said. He expects the company's GoPro revenue to sink to a "very low number" this year.
Making up for the lost revenue are the surveillance (professional and consumer) and auto OEM markets, he noted. Today, the company derives roughly 15 percent of the its revenue from the automotive sector.
Inside the CV1 SoC.
Source: Ambarella
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What Ambarella sees as its ace in the hole, though, is a new CVflow architecture that delivers stereovision processing and deep learning perception algorithms. Ambarella’s goal for CV1 and a series of new computer vision chips based on CVflow (to follow later) is to get a head start in the self-driving vehicle market while capturing other automotive applications, including ADAS, electronic mirror, and surround view.
Alberto Broggi
In the summer of 2015, Ambarella acquired for $30 million VisLab, a startup spun from the University of Parma, Italy. A team led by Professor Alberto Broggi, a founder of VisLab, is the backbone of Ambarella’s AV software stacks for highly automated vehicles.
In Ambarella’s off-site demo in Las Vegas, Broggi showed off two cameras — a short-range monocular (up to a few meters), and a stereoscopic camera for views 150 meters. Both are based on CV1. By applying CNN, a monocular camera can detect and classify objects for known classes like pedestrian, vehicles, motorcycles. The stereoscopic camera detects generic objects —which the camera is not trained to classify — in 3D structures, much like the way a lidar sees things in point clouds.
Compared to a lidar that generates 2 million 3D points per second, Broggi said, the long-range stereoscopic camera captures "800 to 900 million 3D points per second."
Monocular cameras are detecting and classifying objects in yellow boxes.
Source: Ambarella
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Stereo cameras are detecting untrained objects in green 3D structures.
Source: Abarella
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Asked if Ambarella's computer-vision chip could replace a lidar, Broggi said that, although this is possible, it's not necessarily the company's intention.
NEXT PAGE: The Secret of CV1