The years from 2006 to 2008 gave rise to a unique state of mania among brand marketers -- a state by which this author was happily seduced. The virtual world, "Second Life," captured the imaginations of both marketers and consumers alike, but the barriers to entry were high, and the user experience was cumbersome. There is still a place for virtual worlds, but the mania among the marketing community has died down (or should I say, "has come to a screeching halt").
Another phenomenon has gained popularity in marketing circles -- augmented reality (AR). Although the term has been around since the 1990s, marketers began to pick up on the technology circa 2008. A cousin to virtual worlds, AR has proven to fit more neatly into the lives of the everyday consumer, and after years of use by brand marketers, AR appears to be here to stay. As someone who spends a great deal of time studying the cross section of technology and human behavior, I urge you to keep an open mind when considering the potential of AR -- 2012 will be a big year.
Have an understanding of how AR works
The typical AR application takes a tremendous amount of computing power. There are different ways to execute AR, some more processor-intensive than others. It is important to understand that there are parallel processes at work no matter what type of execution you are employing -- all of which are a drain on today's average smartphone. Before we go any further, let's take a look at basic components of traditional AR.
(Sourced from http://www.cescg.org/CESCG-2011/sites/El-ZayatMohamed/.)
First, a camera has to detect a specific shape, image, or object in order to ascertain what is meant be augmented. There are other triggers for AR such as GPS, but for simplicity's sake, we will deal with computer vision in this article. You may have seen AR executions in which a black and white marker is used; this method is employed because a unique pattern with stark contrast takes less computing power to detect. Older, less powerful smartphones are not capable of detecting more complex, natural features. Markerless AR is growing in popularity, but as one might guess, detecting complex patterns is more difficult than a black and white marker. As you can see in this demo, the first thing the camera is attempting is pattern recognition:
Once an image or object is recognized, a digital overlay is positioned in relationship to what is detected. The complexity of the overlay will determine how hard a processor will have to work. A single core processor is often not powerful enough to get the job done (we will talk about devices and processors in the next section). 3D models with large numbers of polygons (poly-count) make it difficult for an augmentation to render, and often slow down the process, resulting in a poor user experience.
Many of the popular AR applications do not accurately track to the object of origin -- tracking is very difficult to do but generally makes the experience much more meaningful. Tracking means when the camera moves, the augmentation moves relative to what it is positioned to, as we saw in the first video. Metaio, an AR software provider, has taken AR tracking to new heights with what they call "gravity-aware" AR. Have a look.
As marketers and advertisers, we aim to create magical experiences that capture the imagination. The more sophisticated the AR execution, the greater chance we have to suspend disbelief and create a sense of awe in the minds of our customers. Early examples of mobile AR were less than magical. Take Yelp's Monocle for a test drive. Although innovative for its time, the user experience is less than inspiring. As AR becomes part of our everyday lives, the ability to surprise and delight our customers with the mere presence of AR will diminish -- but for now, there is still an element of magic in the technology itself.
