The low-voltage, high-performance OV7962 provides the entire functionality of a single-chip digital VGA sensor and has extremely low light sensitivity. Although its VGA resolution is on the low side for traditional machine vision, the chip incorporates advanced image processing functions, such as automatic exposure/gain control, lens correction, defective pixel correction, white balance, and horizontal and vertical windowing capability.
As a reference module, the SmartVue gives developers the ability to produce a proof-of-concept design for testing algorithms and applications early in the product development cycle. It comes with a comprehensive software suite: the Image Cognition Processor Software Development Kit, the OpenCV-based Imaging Tool Kit, various application tool kits, and code development tools.
The camera module is expected to enable the development of several different types of embedded vision applications that execute real-time image processing and scene analysis. These include automotive driver-assistance systems, control of consumer appliances with gesture recognition, using face/people detection and tracking to measure audiences, smart video monitoring, asset monitoring, sensing occupants in "smart" buildings, military uses for body-worn cameras, and robotics.
Although traditional industrial machine vision inspection applications aren't mentioned in the company's list of target applications, there's a parallel trend that may well take advantage of this tiny embedded smart camera. That's the growing use of smaller and smaller portable inspection systems that can be moved around the factory, for uses such as incoming and outgoing inspection.
I said earlier that embedded vision is a big, yet mostly unknown, trend in traditional machine vision. The Embedded Vision Alliance, founded in May 2011, was created specifically to address this trend, which is the result of converging technologies and of shifts in image sensor price/performance thresholds. As Jeff Bier, president of founding member company BDTI, said in an article I wrote for Test & Measurement World, embedded computer vision remained in low-volume applications like industrial machine vision until recently because it was based on historically expensive, low-volume, specialized technologies. But around 2005, image sensor performance went up as prices came down, much of this fueled by the success of cameras in mobile phones. And then came the typical semiconductor/electronics technology curve: Volumes go up, prices come down, volumes go up again.
I've been writing about technology long enough to not get too excited about new product or technology announcements. But this one is different. I think embedded vision is about to become a very big deal.
btwolfe - Just to clarify, the SmartVue development camera module uses OV7962 a wide dynamic range VGA sensor (not 7690). The CV2201 Image Cognition Processor in the camera (the brains so to speak) is sensor-agnostic and can interface to a number of different sensors including megapixel.
Tina - The only difference I see between the 7962 and the 7690 is the MIPI interface and support for 50/60Hz illumination compensation. Regardless, I think they would have gotten more milage from a better imager. Perhaps a future rev of the cube design? Incidentally, I only noticed this because I'm working on a similar compact imager concept, except that my system does passive stereo processing to generate depth information. Of course, it wouldn't be the same small form factor, but the all-in-one concept is the same. It's good to see products like this come to market.
Charles, my understanding is that existing systems do the processing inside or near the camera as well. These systems are however still larger than a CogniVue-based BSD solution.
Tina, thanks for all the input on the SmartVue camera, especially from the app development perspective. My experience accords with Jon's, that in vision system engineers are interested less and less in coding and more and more in faster, easier app development.
Spot on, Chuck. I also see potential applications in perimeter protection and in airport and city center security. Most of use know about London's 10,000 cameras (or whatever the specific number is), which monitor activity to keep an eye on crime and terrorist threats. For perimeter and airports, the TSA stuff we see isn't where the cutting-edge research activity is. Here's a piece I did a couple of years ago about some interesting IBM stuff. (Who knew IBM was into perimeter and airport protection?)
From someone in the automotive camera business, yes, this is 'old hat'. One inch square was the old standard. The new form factor that we are designing to is a 18-20mm sided cube. The automotive smart cameras tend to have a module with the imager on it, video goes parallel out of the imager chip into a DSP on the next board of the camera. Usually, it is located very near the camera to avoid signaling issues. In the case of a front view smart camera, the lens peek out of the windshield above the rear view mirror. The DSP is on on a circuit board directly above it. I think most car cameras, rear view or forward view (usually a smart type), are based on 1/3" imagers. Now we are moving on to 1/4, 1/5" and smaller. This is where the German car camera market is at currently.
The difference in requirements between cameras for automotive applications and macine vision, for inspection or gaging, are large. Watching for a car in a blind spot, keeping an eye on the lane edge marker, or checking the position of the right-side passenger are much easier than inspecting a part for proper threads or correct dimensions. Also, dtermining part orientation is a demanding application as well. My point being that the two applications are very different and as a result, comparisons between them, (the two different types), are of marginal value.
Craig, thanks for the info. A question: were all the specs you discussed from the German car market? If so, were they for high end cars or for more mainstream vehicles?
These cameras are and will be for the hi end market, Audi, BMW etc. One of the basic differences that I see between machine vision and automotive is that, with the forward looking smart cameras, the requirements are the camera be spacially accurate. With machine vision, the camera must be accurate enough to do the job in 2D. With hi end automotive vision applications heading towards being able to do dynamic collision avoidance (moving car vs. moving object), the modern camera must work with scene recognition, the 3D brother of 2D pattern recognition. So one camera, using multiple frames of video will generate a moving 3D 'map' of the scene ahead, two cameras are not required, which simplifies the calibration and hardward required. Scene recognition for automotive aplications is a new frontier, obviously the robot industry has been working on it awhile. The autonomous vech. competition was very interesting. Next the car will have to figure out if the object is okay to run over or, apply the brakes determining that the car behind has time to brake as well! ;^) And that joke alludes to the newest systems for autos that allow a top or adjustable 360 degree view of the car on the dash display.
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