The Future of Analytics
Gene Frantz
TI Principal Fellow and Business Development Manager, DSP
Smart, programmable cameras with a high degree of analytical intelligence will be the workhorses that enable the next generation of applications for the security and automotive markets to name just a couple.
Smart cameras will constantly be absorbing, processing and acting on information all around us, so we are free to think about other things. If someone climbs over a fence at 1:00 in the morning, these cameras will be able to distinguish between your teenage son sneaking home or a burglar trying to sneak off with your TV set. In an airport or other public places, they will know if a bag has been left unattended and will contact the appropriate authorities. On the streets, they will reduce the work load on the police force and other emergency services. In our cars they will serve as a security function but will also eventually take over the driving responsibilities. (Read my entry on, “Security vs. Safety and Privacy”)
Currently, when we think of video analytics, we think of a human as the end user. We think in terms of receiving information in a storable size that still contains all the data, of compression and reproduction. But the future of analytics will feature a computer as the end user. In that scenario, compression is no longer an issue and a perfect picture is unimportant – to a machine, a bad pixel is merely a spec of dust to be ignored. Analytics becomes less about data and more about intelligence, and that is a huge step that will require not just technological transition, but sociological transition as well. People may not be quite ready for machines to make important decisions without any human input. (Another good one to read, “Is Technology getting more Personal or Intimate?”)
I think this transition will occur in steps. Take the automotive industry for example. First, we will put cameras in to allow drivers to make better decisions. For instance, a camera can look at a driver’s blind spot and pass along the data of whether a car is there or not. The driver makes a decision about what to do with that data. In the second iteration of the technology, the camera becomes the second opinion on a decision. If I try to change lanes and the camera sees a car in the other lane, it tightens the tension in the steering wheel to let me know there is a potential problem. Third, the camera becomes the leading decision maker and the driver has to option to override it. Ultimately, we will reach a point where the camera is the only opinion and decision maker. (“Is There a Consumer Breaking Point In Terms Of Convergence?” )
Of course, all of this is going to require programmability. Not only because the system has to be instructed in the myriad of decisions it will have to make, but also as a reality of business. We will need cameras that are programmable and able to have the software adapted according to the application, which will continue to evolve with time. But the bottom line is, as long as you have enough programmability, there’s no limit to what you can do. (SoCs Are Answering Demand for Converged and “Architecture or Application?”)
Of course, we humans will need some re-programming as well. We have to be able to trust these cameras and the decisions they make.
But once we do, we will see these smart cameras used in every aspect f our daily lives. They will be used by the department of transportation to detect not only the location of traffic problems, but the root cause. They will be used for every form of security, from facial recognition in airports, to guarding newborn infants in hospitals. They will be used to transform transportation as computers relieve us of the burden of driving, thus ending gridlock, accidents and delays.
The technology will be in place. Are we ready for it as human beings?
This entry may also be found on SemiApps.com