Vertical-Specific Video IoT Solutions Drive New Use Cases in Retail and Hospitality, but Storage and Legacy Cameras Remain a Challenge

The video Internet of Things (IoT) solutions market is quickly growing as video cameras move past traditional security use cases and become familiar, operational tools in everyday spaces like parking garages and grocery stores.

Two years ago, interest in Artificial Intelligence (AI)-enabled video cameras in the IoT industry was intense, but reserved. Mobile operators and video analytics companies understood the technology’s diverse potential, but realized most camera deployments at the time were reserved for security purposes or expensive operational use cases in manufacturing. Intelligent video cameras for operational efficiency in more familiar, consumer-based markets like retail or hospitality was an exciting possibility reserved for the future.

Since then, cloud platforms have decreased the cost of video analytics and AI-equipped products of all kinds have become more socially acceptable in almost every field, driving greater interest in intelligent video use cases in new markets.

Several IoT Mobile Virtual Network Operators (MVNOs) view video surveillance as a target application for 2025, signaling that demand is increasing among IoT users. This February, for example, 1NCE announced a high-data offering, seemingly in response to growing interest in video-led use cases.

This increasing demand indicates that cameras are finally becoming the business process tools the market thought they could be years ago. Until recently, operational use cases for cameras were reserved for expensive Operational Technology (OT) markets like supply chain or manufacturing. In these verticals, an AI-equipped camera can automate operational tasks on the factory floor or in the supply chain journey, spotting defective products or tracking goods from cargo ships to warehouses. These use cases, particularly in manufacturing, often require expensive hardware and software so that a camera can be trained on and eventually recognize a customer's specific products.

These continue to be important operational use cases for cameras, but improvements in cloud and sensor technology have allowed video analytics use cases to expand past these complex, OT-minded verticals and into Information Technology (IT) markets, like retail and hospitality.

New verticalized, operational video solutions are often prepackaged and specifically designed for a single industry. For example, RetailNext sells proprietary sensors and video analytics software to guarantee relevant insights for retailers, such as foot traffic monitoring. Similarly, Metropolis is a computer vision company specifically targeting the parking industry. The company uses cameras and proprietary software to allow drivers to park in parking garages without checking in or out. The company is rapidly scaling and recently acquired SP Plus Corporation, a leading parking network in North America. Metropolis' offerings are similar to the technology that Amazon believed would disrupt the grocery store market. Amazon’s “Just Walk Out” sensor and camera technology allows customers to purchase items within its participating stores without having to physically pay for them.

Unlike the traditional machine vision use cases in manufacturing, which must often be tailored to a specific company’s products, these verticalized, operational solutions are relatively agnostic to a single company or site and are instead tailored to a whole industry. This one-size-fits-all business model could significantly increase the installed base of operational video cameras in the future.

As these new, sleek, and verticalized video technology companies continue to disrupt legacy verticals like parking garages, they will still have to contend with the traditional inhibitors of the video surveillance market, including customer storage concerns and legacy camera deployments.

Most businesses have video surveillance cameras, many of which might have been purchased years ago for security purposes. Newer operational companies with camera or sensor solutions like RetailNext will have to convince many of their customers to purchase new camera hardware for purposes that might still be unfamiliar to users. It is imperative that these verticalized machine vision companies can communicate the value add, the cost-savings, and the reliability of their product to persuade companies to buy new, and likely more expensive, camera and software technology.

Video surveillance customers also continue to struggle to store surveillance footage. Some video camera deployments run for 24 hours a day, collecting reams of film that have to be stored sometimes for months or even years depending on government regulations. Adding new cameras for operational purposes will increase the amount of footage a customer produces. These newer cameras also have higher resolutions because they must capture objects like license plates clearly for analysis, resulting in even larger data files for customers to store. Many of these machine vision companies likely have cloud storage as a part of their offering, but depending on the product or sensitivity of the footage collected, some customers might also request to host footage on-site to ensure that the data can easily be retrieved. Newer video IoT solution companies targeting operational use cases will need to be clear about their storage capabilities and offer customers flexibility for both off-site and on-site storage, particularly if they are selling into more sensitive IT markets like schools or hospitals.