It seems you just can’t escape a camera these days.
Rewind to the past…the camera pointed at you was, more likely than not, a surveillance camera aimed at detecting crime or for some form of monitoring, or a security camera that was part of a system for ensuring public safety.
Come to the contemporary world…the camera is more likely to be collecting data that will end up being used anonymously in multiple ways to make your life simpler and more enjoyable.
“Anonymously” might sound negative, but here it actually refers to the fact neither the system nor the camera has to know who you are or what you do. When it is serving a good purpose and things are done in compliance to data privacy laws, what matters is that such data capture can be immensely society-friendly if the intent is good.
Take a simple example. A metro rail system may be using Cameras, powered by Artificial Intelligence and Computer Vision, to analyse traffic congestion on the platforms. The video analytics run on the data captured by the cameras may help the system adjust schedules or make operational changes that will enhance customer service. Data can be collected in such a way that it can never be linked to an individual, even if the software identifies the gender or age-group or the kind of dress he or she is wearing.
Elsewhere, the camera network on a factory floor may be simply helping to keep workers out of danger around active machinery or collecting data on their movement patterns. Today, by studying people’s behavior in several related situations, Computer Vision and AI help understand and predict human-machine interaction more accurately than ever before. Pro-active measures based on such data helps eliminate unsafe practices or work flow bottlenecks.
Technologies like Cogniphi AI Vision have enabled leveraging an existing security camera system to create dedicated video analytics platforms that benefit a wide range of industries, from manufacturing to retail to transportation to health to smart cities. If they are designed for Data privacy compliance and runs within a private and secure environment, these technologies can produce highly accurate and protected data that is committed to maintain anonymity. And they require very little hardware investment to deploy.
It’s the demography that matters
When a Video Analytics platform, say, tracks a Retail shopper, it can filter out personal data like facial features or how they walk, and focus on collecting and processing information like age and gender, buying patterns, time spent on comparing products, behavior and emotions at point of sale, and so on.
For the retailer too it is user behavior at a demographical level, more than unique identification, which will provide insights that matter to him. The store, for example, may want specific information on the type of shoppers usually passing through Aisles 5 and 6 so that it can plan targeted advertisements there aimed at a particular type of clientele.(For more info : AI Vision for Retail industry)
In the end, it is a win-win for both the retailer and the visitor. The retailer gets to understand customer behavior so that he can design better and more relevant promotions, marketing initiatives, A/B testing and performance tracking. The visitor gets overall a more interesting, pleasant and engaging experience.
Impact on RoI
Users of technologies such as Cogniphi AI Vision are often able to make decisive and impactful moves soon after they realize that the data thrown up by the software is consistent, reliable and accurate. Some of these insights might actually challenge several assumptions but they eventually lead to big gains, particularly when these products get to be used in multiple locations.
Ideally the platform must be fully software defined so that capabilities can be added on later and data can be collected on a rolling basis when needed. It is when the customer gets this sort of flexibility that he will start relying on data for most of his decisions and feel the impact on RoI.