In parts 1 and 2 of this post, we examined how mobile networked cameras are proliferating, and how maturing computer vision methods are allowing useful interpretation of what appears in those video feeds.
We now focus on how all of this information, and the improving automatic interpretation of it, can be used to create highly useful collective intelligence for those who choose to participate.
A core resource may be thought of as an "intelligent 4D world model.\" Think of a graphic information system on steroids, knitting together historic and real-time video and other telemetry (GPS location, heading, look angle, and so on). One could enter a visualization of this model and look around, fly, or walk through, dial time forward and backward, or watch current events unfold from a wide range of perspectives in 3D. Any number of other "intelligence layers" could be overlaid, such as topographic maps, communications traffic, detected identities of individuals, vehicles and traffic flows, objects, buildings, and landmarks. For a quick example of 3D knitting of large collections of unregistered imagery, see the TED talk on Photosynth software. Google Earth and Maps already combine several such sources of information, but such a resource could be deeply enriched by intelligently processed real-time video from millions or billions of cameras.
Applications could include minimally intrusive monitoring for eldercare, child tracking and protection, intelligence for various stakeholders in the real estate market or for those planning a business venture, real-time reporting and monitoring of criminal activity, \"reputation services\" that allow citizens to comment upon especially good or bad acts on the part of others and disseminate that information to other subscribers, implementation of favor networks and soft equity, and opportunity-driven micro-job outsourcing (like Amazon Turk, but much more dynamic and situational). Intelligent back-end services will eventually even start organizing subtle aspects of voluntary production and consumption across their subscriber base so that everyone gets what they want, perhaps even before they know that they want it.
Without the right kind of data and its intelligent interpretation, none of this could happen, but this is where we are heading.
The author's affiliation with The MITRE Corporation is provided for identification purposes only, and is not intended to convey or imply MITRE's concurrence with, or support for, the positions, opinions, or viewpoints expressed by the author.