The significance of video AI in the advertising and marketing sector

Updated: November 13, 2021 3:14 PM

Secondary monitoring is considered revenue generation monitoring which provides inferences from various activities happening in stores

Artificial intelligence- powered video analytics has arrived at a stage that can revolutionise anything and everything and take the industries to an exceptionally high ROI.

By Atul Rai

Artificial intelligence-powered video analytics has gained traction to the extent that it has become buzzwords of the 21st century. Right from navigation to security, facial recognition, and even vehicle analysis, AI-based technologies have revolutionised our lives and are continuing to amaze us. As per a report, more than a billion cameras were sold last year. Also, Indians sold 30 million CCTV cameras in 2019-20 with a 20% CAGR. Research says that we have more cameras than humans on the earth. So automating CCTV surveillance is certainly beneficial in several ways.

We install cameras broadly for activity monitoring, vehicle monitoring, person monitoring, and object monitoring. And this monitoring is done by humans themselves. The challenge with human monitoring is that we cannot focus on a particular screen for more than 20 seconds or 30 seconds because of various psychological facts. So the problem is that we are dependent on human capabilities for monitoring and human is not even efficient in that. So that is where automation is needed. Camera monitoring is bifurcated into two parts –

1. Loss prevention or direct monitoring
2. Secondary or revenue generation monitoring

Direct monitoring which we see in manufacturing, infrastructure, retail, or government organizations where activities like accident monitoring, violence monitoring, cleaning mopping monitoring, person detection are monitored. Also, facial recognition where we are doing identification whether it is for attendance or to detect blacklisted/whitelisted persons also comes in direct monitoring. Its purpose is to prevent any loss and is a cost-saving method used for monitoring. For example, if an accident happens that comes with monetary loss or any violence will lead to sabotage in store. This is prevented by sending real-time alerts so that necessary actions are taken to avoid any loss.

When it comes to secondary monitoring it is considered revenue generation monitoring which provides inferences from various activities happening in stores. Stats like the number of people who visited the store, particular apparel they were interested in, at what time there was a hike in the store, footfall gender, etc. are gathered for better decision making. Though video analytics started with a cost-saving monitoring method however it is also working for the revenue generation part. In brick and mortar stores, cameras are installed and also security guards are appointed to prevent any kind of intrusion or illegal activity. So one investment is done on camera infrastructure and another on human force to monitor it. As the world population is huge you can’t put humans behind every monitoring to avoid security breaches. This monitoring method needs to be automated. Take an example of the manufacturing sector where safety jacket checks, loading /unloading time, a vehicle coming in/out, pilferage monitoring or we can say the whole manufacturing ecosystem operations and safety issues are solved using AI-powered video analytics.

Let’s take another example from the retail sector. Currently, the competition of retail stores is with e-commerce. Ecommerce is having the luxury of all the data with them. One of the major reasons why e-commerce websites are winning the retail race is because of their ability to track the daily customer inflows, dwell time, and purchase choices through an automated system. They know exactly what to offer to their customers the next time they open their virtual store.

Therefore, some key customer analytics which is crucial in making important decisions for marketing, operations, inventory control, etc. are not available with the offline stores. Offline stores have blind sensors which are unable to give insights like the time at which store receives more footfall if a store has invested in planogram for occupancy analytics what output a store is getting, customers are actually visiting the strategised aisle or not, demographics of customers visiting a store, etc. All this information is not with offline stores and they are investing money blindly in marketing. With video analytics retail operators get that data making their marketing campaigns more mature and getting more revenue on that.

The camera is the most human brain-like interpretable sensor. For example, the sensor can detect fire and for authenticating the alert generated by the sensor a person has to visit that particular site however a camera gives an alert, the only thing required is to see the picture, and necessary actions are taken irrespective of your location. The feature that makes CCTV surveillance very powerful and economic is that it is using the existing infrastructure and makes cameras smarter by using the top layer of artificial intelligence which is video analytics. So, adoption of this technology is easy as there is no extra cost to it.

Additionally, whatever results are generated that can be identified by humans is not possible with any other sensor. For example, any sensor which counts the number of objects loaded/ unloaded on a truck how will that be verified unless humans will not verify that.
That is the benefit of the camera. Also, a camera can do multiple things, the same camera is identifying violence and is counting objects too. The camera is ubiquitous which makes it the best tool in automating various solutions. In nutshell, we can say that artificial intelligence-
powered video analytics has arrived at a stage that can revolutionise anything and everything and take the industries to an exceptionally high ROI.

(The author is co-founder and CEO, Staqu. Views expressed are personal.)

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