The Future of Embedded Vision Systems

Embedded Vision Is Changing How Machines and People Interact With the World

The future of embedded vision systems looks bright – there’s commercial potential for embedded systems in virtually every industry. This technology has already proved transformative in the automotive, consumer electronics, medical, and robotics and automation industries, among dozens of others. The future of these industries will be impacted by the evolving imaging capabilities embedded vision affords.

The next frontier for embedded vision involves the integration of 3D perception and deep learning resulting in previously unimaginble levels of visual perception. It’s clear the combination of these technologies will be one of the primary ways that embedded vision capabilities advance and evolve over time, enabling entirely new vision applications across industries.

Hardware and Software Improvements Are Needed For Future Potential to Be Realized

While 3D perception, deep learning and embedded vision have incredible potential in a variety of futuristic applications, these technologies are only just beginning to integrate. Before deep learning algorithms can become a common feature of embedded vision systems, hardware and software components must improve to meet the power, energy consumption and costs that come with deep learning capabilities.

In a nutshell, computer vision needs more capable and reliable algorithms and software development productivity must increase to keep costs low. Processors will need to continue gaining computing power without sacrificing energy-efficiency or their initial price tag if deep learning is ever to become widely accepted and commercially viable. In a similar way, the upfront cost of image sensors needs to decrease as efficiencies continue to increase.

Although the technology behind deep learning techniques certainly has room to grow, and 3D vision technology has yet to find its way into all imaging applications, it’s clear that 3D perception and deep learning will pave the way for the future of embedded vision systems. The main obstacles to widespread deployment are energy consumption and costs – two important areas of focus for on-going developments.

What Types of Vision are Possible with 3D Perception and Deep Learning Right Now?

Deep learning techniques, combined with 3D imaging, build upon and improve existing imaging techniques. For example, determining optical flow, or the estimated movement of an object in a frame, is possible with simple 2D computer vision systems. Now, 3D embedded vision systems backed by deep learning algorithms can estimate an object’s path with far greater accuracy while continuously refining its own results.

Object classification is another common imaging technique enabled by deep learning, and possibly one of the most popular. Embedded systems are often “trained” with huge repositories of image data with the goal of teaching an algorithm to differentiate between objects (i.e. to identify a dog as a dog).

At one point, object classification was a revolutionary concept, but with today’s technology, it’s a fairly elementary procedure. Beyond classification, deep neural networks with access to 3D visual data enable object detection, which is where an embedded system can not only classify an object but determine its location in space.

Deep learning and 3D vision have also enabled semantic segmentation, where an algorithm can separate individual elements in an image and then assign a value to each pixel that identifies to which object that pixel belongs. This is a key imaging capability for a number of embedded vision applications that require some degree of object recognition.

When deep neural networks have access to a wealth of 3D visual data from embedded vision systems, entirely new possibilities begin to emerge.

Future Embedded Vision Applications

For embedded vision systems, 3D imaging capabilities and deep learning techniques lay the foundation for the future. From there, most applications will rely on these two technologies in one form or another for entirely new imaging applications.  

One future potential application that involves deep learning and 3D vision is in robotics. Robotic guidance is already used to avoid collisions and guide vehicles around warehouses, but neither of these involves deep neural networks. Theoretically, with proper deep learning techniques, robots could work with parts they’ve never seen before, dramatically changing the potential uses of robotics. For example, a pick and place robot could be moved to an assembly line with parts it’s never before seen and quickly begin to successfully pick parts. By nature, robots complete repetitive tasks quickly and consistently, but future embedded vision technology offers so much more.

Facial recognition will be another important embedded vision application of the future. While there are forms of facial recognition available today, they have not nearly reached their full potential, especially for challenging outdoor applications like security and surveillance. One area facial recognition will likely be used is for advertising feedback and personalization – not only judging people’s reactions to ads but gauging their mood to serve more relevant ads based on what they may be interested in at that moment.

Embedded vision sensors of the future may combine to create highly specialized simultaneous localization and mapping (SLAM) systems. SLAM is essentially used as a GPS system, except for the fact that it uses an array of sensors to determine location and create maps of a physical space with far greater accuracy than GPS systems. These systems have potential in autonomous vehicles as well as a variety of augmented reality applications to improve the accuracy of both.

Augmented reality (AR) and virtual reality (VR) will rely heavily on the future capabilities of embedded vision systems. AR will depend on embedded vision systems to accurately identify, map and recognize the world around them to quickly and seamlessly overlay digital imagery onto what a person is seeing.

In VR applications, one promising method of building virtual worlds is by recreating the physical one. Advanced imaging recognition techniques could be used to precisely replicate the physical world in a virtual environment, opening up the door to entirely new types of telepresence and virtual interactions. Both AR and VR are literally changing the ways we interact with the world around us, and embedded vision systems will play a vital role in this process.

Embedded Vision Systems Are Transforming Entire Industries

In many instances, especially in future applications, embedded vision systems are the link between the physical world and the digital world – through a wealth of 3D visual data informing deep neural networks they enable complex algorithms to act on the physical world.

Embedded vision systems have incredible potential, both for current applications and for future technology, in no small part due to 3D perception and deep learning capabilities. While embedded vision will certainly improve in other ways, these two technologies will undoubtedly play primary roles in the evolution of embedded vision technology.

Embedded vision is maturing into a highly disruptive technology. It is already present in a wide range of industries and continues to emerge in entirely new ones. The future of embedded vision systems is exciting – promising significantly changes in the way in which we, and our machines, interact with the world around us.