If you have seen a demonstration of Audi’s Automated Parking technology in which the car autonomously finds a parking spot and

parks itself without a driver—or if you have played an Xbox 360 game with its Kinect controller or even just bitten into a flawless

piece of fruit you purchased from your local grocery store—then you can count yourself as an eyewitness to the dawning of the

era of smarter vision systems. All manner of products, from the most sophisticated electronic systems down to the humble apple,

are affected by smarter vision technologies. And while today’s uses of smarter vision are remarkable enough, experts say we

haven’t seen anything yet. Some predict that in 10 years’ time, a vast majority of electronics systems—from automotive to factory

automation, medical, as well as surveillance, consumer and aerospace and defense—will include smarter vision technologies with

even more remarkable capabilities that will dramatically enrich and even save lives.

Smarter vision systems will soon become ubiquitous, and as they increase in complexity over the coming years, we’ll very likely

become passengers in autonomous automobiles flowing in networked highways. Medical equipment such as Intuitive Surgical’s

amazing robotic-assisted surgical system will advance even further and may enable surgeons to perform procedures from remote

locations. Television and telepresence will reach new levels of immersion and interactivity, while the content on screens in theaters,

homes and stores will cater to each individual consumer’s interests, even our moods.

Xilinx

All Programmable solutions for Smarter Vision are at the forefront of this revolution. With the Zynq™-7000 All

Programmable SoC—the first device to marry an ARM

dual-core Cortex™-A9 MPCore™, programmable logic and key peripherals

on a single device—as the foundation, Xilinx has fielded a supporting infrastructure of tools and SmartCORE IP that will play a

pivotal role in enabling the development and faster delivery of these remarkable innovations to the world. The supporting

infrastructure includes Vivado™ HLS (high-level synthesis), the new IP Integrator tools, OpenCV (computer vision) libraries,

SmartCORE™ IP and specialized development kits.

“Through Xilinx’s All Programmable Smarter Vision technologies, we are enabling our customers to pioneer the next generation

of smarter vision systems,” said Steve Glaser, senior vice president of corporate strategy and marketing at Xilinx. “Over the last

decade, customers have leveraged our FPGAs to speed up functions that wouldn’t run fast enough in the processors they were

using in their systems. With the Zynq-7000 All Programmable SoC, the processor and FPGA logic are on the same chip, which

means developers now have a silicon platform ideally suited for smarter vision applications. We’ve complemented the Zynq-

7000 All Programmable SoC with a robust development environment consisting of Vivado HLS, new IP Integrator tools,

OpenCV libraries, SmartCORE IP and development kits. With these Smarter Vision technologies, our customers will get a jump

on their next design and be able to achieve new levels of efficiency, lower system power, increase system performance and

drastically reduce the bill of materials—enriching and even saving lives while increasing profitability as these innovations roll out

at an ever faster pace.”

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From Dumb Cameras to Smarter Vision

At the root of Smarter Vision systems is embedded vision. If you are unfamiliar with embedded vision, let’s look at what this

technology is and how it is evolving.

Embedded vision, as defined by the rapidly growing industry group the Embedded Vision Alliance (www.embedded-vision.com/), is

the merging of two technologies: embedded systems (any electronic system other than a computer that uses a processor) and

computer vision (also sometimes referred to as machine vision).

Jeff Bier, founder of the Embedded Vision Alliance and CEO of consulting firm BDTI, said embedded vision technology has had a

tremendous impact on several industries as the discipline has evolved far beyond the days of motorized pan-tilt-zoom analog

camera-based systems. ��We have all been living in the digital age for some time now, and we have seen embedded vision rapidly

evolve from early digital systems that excelled in compressing, storing or enhancing the appearance of what cameras are looking

at into today’s smarter embedded vision systems that are now able to know what they are looking at,” said Bier. What’s more,

cutting-edge embedded vision systems, or smarter vision systems, not only enhance and analyze images, but also trigger actions

based on those analyses. As such, the amount of processing and compute power, and the sophistication of algorithms, have

spiked dramatically. A case in point in this remarkable evolution is the rapidly advancing market of surveillance.

Twenty years ago, surveillance systems vendors were in a race to provide the best lenses enhanced by mechanical systems that

performed autofocus and tilting for a clearer and wider field of view. These systems were essentially analog video cameras

connected via coaxial cables to analog monitors, coupled with video recording devices monitored by security guards. The clarity,

reliability and thus effectiveness of these systems were only as good as the quality of the optics and lenses, and the diligence of

the security guards in monitoring what the cameras displayed.

The advent of embedded vision technology allowed surveillance equipment companies to use lower-cost cameras based on digital

technology. This digital processing gave their systems extraordinary features that outclassed and underpriced analog and lens-

based security systems.

Fisheye lenses and embedded processing systems with various vision-centric algorithms dramatically enhanced the image the

camera was producing. These techniques, which correct for lighting conditions, improve focus, enhance color and digitally zoom in

on areas of interest, also eliminated the need for mechanical motor control to perform pan, tilt and zoom, improving system

reliability. Digital signal processing has allowed companies to offer surveillance systems at video resolution of 1080p and higher.

Indeed, in the case of unmanned aerial vehicles (UAVs) and defense satellites, embedded vision has enabled far higher resolution

than historically available. Simply capturing images at this resolution requires a tremendous amount of pixel processing. Enhancing

and manipulating these images requires even more processing horsepower.

But a clearer image that can be manipulated through digital signal processing was just the beginning. With considerably more

advanced pixel processing, surveillance system manufacturers began to create more sophisticated embedded vision systems that

performed analytics in real time on the high-quality images their digital systems were capturing. Every year, vision systems

designers field a more impressive array of sophisticated algorithms to create increasingly dynamic analytic features. The earliest of

these embedded vision systems had the capacity to detect particular colors, shapes and movement. This capability rapidly

advanced to algorithms that detect whether something has crossed a virtual fence in a camera’s field of view; determine if the

object in the image is in fact a human; and, through links to databases, even identify individuals.

The most advanced surveillance systems include analytics that track individuals of interest as they move through the field of view

of the security network, even as they leave the field of view of one camera, move into a blind spot and then enter into the field of

view of another camera in the surveillance network. Vision designers have programmed some of these systems to even detect

unusual or suspicious movements. “Analytics is the biggest trend in the surveillance market today,” said Mark Timmons, system

architect in Xilinx’s Industrial, Scientific and Medical (ISM) group. “It can account for human error and even take away the need for

diligent human viewing and decision making. As you can imagine, surveillance in crowded environments such as train stations and

sporting events can become extremely difficult, so having analytics that can spot dangerous overcrowding conditions or track

individuals displaying suspicious behavior, perhaps radical movements, is very advantageous.”

To further enhance this analysis and increase the effectiveness of these systems, surveillance and many other markets leveraging

smarter vision are increasingly using “fusion” architectures that combine cameras with other sensing technologies such as thermal

vision, radar, sonar and LIDAR (Light/Laser Detection and Ranging). In this way, smarter vision designers can further enhance the

resulting image to enable night vision; detect thermal/heat signatures; or pick up objects not captured or not visible by the camera

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