By Rajasekhar Papolu, Chairman and Managing Director, Brihaspathi Technologies Limited
A tiny vibration on a factory conveyor belt is barely registered by the human eye. But an AI-enabled camera caught it instantly, flagging a slight misalignment that could have stopped production for hours. The incident never made it to a report because the intervention occurred before anything went wrong. That is the difference between systems that merely watch and systems that truly understand.
Video, once treated as passive footage stored for future review, has entered a new era. Organisations across industries are now treating it as a real-time data layer. Grand View Research notes significant momentum in the global video analytics market, driven by widespread camera adoption, the falling cost of compute, and the rapid rise of edge processing. Cameras are no longer just recording devices; they are becoming operational sensors.
The shift is powered by a set of maturing techniques that convert pixels into meaning. Object detection identifies what appears in the frame. Tracking follows those objects to understand movement and context over time. Behaviour analysis interprets actions whether a person is loitering, crossing into a restricted area, moving in ways inconsistent with standard procedures, or not wearing required safety gear. Anomaly detection goes a step further by flagging deviations from “normal” patterns without relying solely on predefined rules. And multimodal fusion enriches the picture by combining video insights with data from access control systems, environmental sensors, or IoT devices. These functions combined together form a non-stop flow of situational intelligence which can’t be matched by human production in terms of quality.
This intelligence is now showing measurable results across sectors. In security operations, automated alerts help teams detect unauthorized access, suspicious movement patterns or perimeter breaches within seconds. The speed of detection naturally improves response time, reducing dependency on manual monitoring. In manufacturing and logistics environments, video analytics tracks flow, congestion, waiting times, and equipment usage, helping with insights that improve output and reduce operational bottlenecks. Compliance workflows also benefit: from automated attendance verification to PPE detection, the system reduces subjectivity and strengthens audit readiness. Safety teams increasingly rely on video analytics to catch incidents such as slips, falls, near misses or unsafe handling behaviours, allowing them to interfere early. Providers like Daten & Wissen and similar enterprise-focused firms have demonstrated how these scenarios, when automated, translate into fewer incidents and more efficient operations.
Behind these outcomes lies a crucial choice of architecture: where the intelligence should live. Many organisations now gravitate toward edge processing because it delivers low-latency decisions without sending raw video off-site. This is particularly valuable where real-time action is critical. Cloud-based analytics, on the other hand, offer scalability and centralised management — an attractive option for multi-location enterprises. Most organisations, however, settle on a hybrid model: initial detection on the edge combined with deeper analytics, dashboards, and historical reporting in the cloud. This balance offers both immediacy and visibility, making it well-suited to large campuses, factories, smart infrastructure projects, transport hubs and corporate environments.
With such powerful technology, responsibly implementing it becomes important. Research from sources including the ACM Digital Library highlights the governance challenges associated with AI-enabled monitoring. Privacy must be ensured through masking, limited access rights and clear boundaries on what video or metadata is stored. Retention policies need to be tightly defined to avoid unnecessary storage of sensitive data. Models must also be checked for fairness to avoid variable performance across different environments. Security and compliance teams must be able to understand why an alert was triggered, what the model saw, and how confident it was in its assessment.
For CIOs and CSOs navigating this shift, by asking the right questions becomes crucial. Questions on on-device inference where latency is critical, including questions on SLAs governing detection accuracy, become an integral part of the process.These considerations determine whether an organization adopts video analytics as a checkbox upgrade or as a genuine intelligence layer embedded into its operations.
The story of modern surveillance is no longer about collecting footage. It is about extracting meaning. The transition from monitoring to intelligence is already reshaping how organisations secure assets, protect people, ensure compliance and optimise operations. Those who adopt thoughtfully blending edge and cloud, embedding governance, and focusing on high-impact use cases will turn their camera networks into a strategic advantage. Those who treat it as traditional CCTV with added software may find themselves watching the future unfold without actually benefiting from it.
In an era where every frame carries insight, the organisations that learn to interpret those frames in real time will lead the next wave of safety, efficiency and resilience.
The views expressed in this authored article are personal to the author.
