About the Company: CogniVue specializes in embedded vision processing, delivering revolutionary vision processing IP and software for performance-hungry and power-sensitive vision applications. Recently, CogniVue introduced the Image Cognition Processor (ICP) architecture as a solution for a new generation of embedded vision applications. ICP architecture is a new processor architecture designed to significantly accelerate vision processing in deeply embedded systems. In fact CogniVue achieves a >100X advantage in performance per power vs. the best alternative processing architectures.
About Tom Wilson: Vice President of Business Development focused on developing CogniVue’s Image Cognition Processor IP strategy and business including working with IP-licensees and partners
Q. 1. What are the key markets for the gesture recognition technologies?
Tom Wilson: Gesture recognition software and associated specialized hardware is in development and deployment world-wide. It is difficult to estimate the relative share of this development and deployment without more analysis, but roughly speaking I would propose Europe as number 1, Asia as number 2 and US in third place of those 3, in terms of deployment and development combined.
Q. 2. What would be the outlook in the consumer electronic space for gesture recognition after five years?
Tom Wilson: For consumer electronics, embedded vision technology is going to be key for an increased adoption of new user interface approaches. Embedded vision enables new applications and new ways to interact with those applications in products like tablets, smart phones, smart TV’s and small smart cameras in the automotive market. Gesture Recognition is just one part of a larger embedded vision revolution.
Q. 3. What are the current market trends for gesture recognition market and the scenario over the next five years?
Tom Wilson: Gesture Recognition applications will need to be highly reliable to ensure user adoption. Even a very low rate of false positives or misses will cause users to turn off the function. Therefore, gesture recognition algorithmic processing will become more sophisticated and reliable over time. Add to that the layering of other vision applications with Gesture recognition (for example Face Recognition and Augmented Reality), and all of this translates into very high vision processing performance at very low power and cost.This requirement for highly compute-intensive vision processing at high real-time performance and low power is the opportunity we see for our ICP APEX vision processing technology.
Q. 4. How do you see the gesture recognition market adoption for the coming five years?
Tom Wilson:The gesture recognition market will certainly move on from consumer electronics to a broader application set comprised of automotive, healthcare, and digital signage to name just a few. The products will include a range of user interface functions like controlling in-car infotainment, contact-less browsing through x-ray or MRI images during surgeries, and interactive digital displays installed in kiosks and shops.
Q. 5. What are the various issues plaguing the current gesture recognition market?
Tom Wilson:Gesture recognition by itself will have difficulty commanding high value for the consumer. This will place extreme cost pressure on gesture recognition solutions. For example, ask yourself how much you would pay to have gesture recognition on your phone or television. In addition, Gesture Recognition only solves part of the user interface problem. Face Recognition is an important, if not necessary, complement to Gesture Recognition in a living room setting to ensure only one user has control at one time. Face Recognition is a very difficult problem to solve in a living room setting where multiple people are sitting at a distance of 3-4 meters in varying light conditions.
Ideally, the application should support a wide field-of-view (FOV) but the greater the FOV with the lens, the fewer pixels the algorithms have to work with at a given distance. So to have wide FOV, you need to increase the resolution of the camera image sensor, which drives up the compute loading for the algorithms. Then there are the added problems of face positioning, for example the person’s head turned at a range of angles to the camera. This adds to the algorithmic complexity as well.
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