If you haven’t been following the video analytics space for a while, the following two statistics will make you pause for reflection.
The worldwide video inspection equipment market revenue has been steadily increasing on a linear path since 2017, with a projected forecast to reach 2.05 billion US dollars by 2027, while the video analytics market is set to expand multi-fold from 4.55 billion US dollars in 2019 to 37.73 billion US dollars by 2030.
Why are these two statistics interesting? They reflect the systemic shift of video intelligence moving increasingly from a hardware-centric market to a software and data-intensive market.
A third intangible statistic has been unfolding in recent years, which might put an entirely new perspective on how hardware and software spend in the video intelligence category: the impact of AI - and generative AI in particular - on video intelligence.
The status quo of AI in Video Intelligence
Today's live video streaming market is driven by the advent of high-quality (and often proprietary) camera systems serving surveillance and monitoring scenarios. Most of these scenarios are realized with the help of network video recording (NVR) technology, enabling network-connected edge devices to stream video data to on-device or cloud storage. The video data, thus captured, is then available for further processing on the software end and consumption for the scenarios that the business needs.
This is where the consumption of video data versus other formal forms of data has fallen behind the times. For context, structured data sets like databases have been liberated for a while from their original data sources (ERP systems or CRM for the most part) with the use of APIs or sophisticated data pipelines which can federate the use of these data sets. This approach allows multiple stakeholders within the business to consume and visualize them within their applications. As a result, it has been easier for business owners to make their critical data 'shoppable' or consumable by any part of their business to glean insights and improve their business operations.
But video data has not seen the processing renaissance that structured data has seen, especially in smaller businesses. As a result, most video data use cases have been around conventional monitoring or surveillance scenarios, with a generous sprinkling of elementary AI applications for object detection and face detection.
Until the past 18 months showed the world what multimodal AI applications could do with the data at hand.
The advent (and promise) of generative AI in video intelligence
With multimodal AI applications, you can ask or prompt AI applications to generate text or video with a series of queries or 'prompts' (with text, images, or video combined) to the AI engine. OpenAI's recent releases in the past two years and the visual prowess of applications like Midjourney and Runway have opened up the promise of multimodal AI applications. Although the technology is still nascent, it is evolving rapidly, giving a lot of creative use cases new life, with new forms of storytelling and visualization made possible through intentional prompting for general use cases to build knowledge and foster creativity.
What is still not completely evident is what generative AI could do with discrete video data inputs for internal enterprise use cases and provide insight into video applications, which was impossible before. Let's explore one such use case in today's scenario: video monitoring in gyms.
If you are a gym owner, you might have been using camera systems in your gym for equipment maintenance or monitoring for the safe usage of gym equipment. For example, in a customer injury-related incident, you might be going back to recorded footage to investigate if the customer in question had been using gym equipment safely and appropriately as instructed within the manual and could defend against customer claims.
In an AI-enabled scenario with real-time video streaming insights, your video intelligence system would be able to detect negligent usage of gym equipment by a customer proactively before an incident occurs and alert you immediately to take remedial action. With a sophisticated AI-enabled video camera system, the applications you use within your gym business could not just caution you on deviant customer activity. It could also help you initiate remediation workflows, which allows you to display how-to exercise videos on the gym floor, which help your customers understand the proper form for the required exercise sets.
If you think these scenarios are far fetched beyond the realm of imagination, that's relatable. Most traditional camera systems today capture high-quality video footage but lock the resulting video data within their proprietary software systems. As a result, the video data and the subsequent insights that machine learning models could unlock from that data are trapped within the walled garden. With an open video system, your workforce could easily tap into the video data in real time and consume them with the applications they use every day to make it actionable.
With an AI-enabled open camera system, your workforce gets video superpowers, as they are now able to summarize the streaming video feed for quick actions like the early onset of gym injuries, and prevent customer claims proactively by initiating workflows directly from their business applications, to help the customer understand proper form.
What happens in a generative-AI scenario then? Think about the possibilities of showing a simulated video of the customer performing exercise sets in the proper form, shown side-by-side with their current form in real-time video streaming. If you can build custom applications around your business to showcase what proactive care capabilities like this scenario could look like, you would be able to provide an unparalleled customer experience, leading to better retention and loyalty for your gym.
How to stay relevant in a world of AI-triggered chaos
Most business owners and IT teams running gym franchises or any business using video equipment face the challenge of blending high-quality camera systems with outdated software capabilities. But suppose an open video management system could free the video data from the underlying hardware complexity and allow your business applications to tap into it. In that case, you can leverage the power of AI and augment it with these early generative AI use cases. Furthermore, suppose the open video system allows your IT team to make this video data consumable within every business application deployed by providing even basic AI capabilities to summarize and derive insights from video. In that case, you have been able to maximize your investment in the open video platform for your business.
Above all, the Spot AI value differentiator has been our ability to build backward from customer feedback, which has enabled the fastest feature velocity in our software platform. This velocity in turning around customer feedback has helped Spot AI become a comprehensive open video system across industries, from car washes and gyms to educational and government use cases.
Book a demo with Spot AI today to learn more about what such an innovative open video system could look like.