Beyond the camera: Why video surveillance is becoming a business intelligence tool

by Dr. Ryad Soobhany, Deputy Academic Head of School of Mathematical & Computer Sciences, Heriot-Watt University Dubai 

Camera systems, such as CCTV, are mainly used for video surveillance within organisations. Passive video surveillance has been part of the security ecosystem for years, providing organisations with the ability to monitor different areas simultaneously, while recorded footage can be inspected after an event occurs.

The evolution of computer chips and increased processing power has led to improved image quality, including better performance in low-light conditions, particularly at night. The advent of edge computing, with processing performed on edge devices within a network, has made object and scene recognition more readily accessible. This evolution has led to more active video surveillance through video analytics, where events can be identified and addressed in real time. Examples include number plate recognition systems and facial recognition for access control.

With the combination of AI and video data, computer vision can be integrated with business analytics to transform video surveillance from a security infrastructure into a business intelligence tool. Video surveillance is moving from passively recording events to sensing what is happening in the environment. This helps to answer questions such as which areas are congested, how customers are moving, how long they dwell in particular areas, and whether unusual events are occurring. Businesses can use this data to optimise their operations, such as improving the customer checkout process, monitoring asset or staff movement, and managing crowds or traffic flows. Organisations can also incorporate contextual business information to enhance the analytics by combining behavioural data with CRM or IoT data, providing additional context and insights for decision-making and improving the customer or visitor experience.

Video surveillance can be used to optimise operations, improve customer experiences, and support strategic decision-making across industries. Video itself does not create business intelligence; rather, its value increases when visual information is combined with other operational and contextual data, while computer vision is used to understand and enhance business operations. The pipeline from video to business intelligence can be represented as obtaining data from video sources such as fixed cameras, IoT-enabled cameras, or robots. Computer vision is then applied to the video to detect or track people, objects of interest, or activities. Behavioural data are taken into account, such as customer dwell time in particular product areas, occupancy of rooms or halls, movement of people across a building, and anomalies in behaviour. At this stage, operational data are also considered. The final stage is the business intelligence stage, where trends are identified and insights are inferred. At this stage, data visualisation plays an important role, with dashboards and data storytelling displaying correlations, predictions, and infographics that provide more than simple visual summaries of surveillance data. The camera becomes an operational sensor for the organisation. The important transformation occurs when visual data are combined with business data, such as sales, operational, and IoT data, to convert visual observations into business knowledge that informs operational, tactical, and strategic decision-making within an organisation.

The levels of business intelligence can be viewed as three analytical capabilities: descriptive intelligence, diagnostic intelligence, and combined predictive and prescriptive intelligence. Descriptive intelligence usually addresses what is happening. Visual analytics can identify footfall, occupancy, queue length, waiting time, dwell time, traffic flow, movement patterns, and detected events. Visualisation dashboards can present these indicators in real time or over historical periods, allowing managers to understand the current state of an operation. For example, a museum dashboard can show heat maps of visitor numbers, dwell time, and movement across different exhibition areas, while analysing visitor behaviour and providing insights into the optimal placement of exhibitions. An airport dashboard could display passenger volumes and queue lengths across terminals.