What Is Video Analytics in Video Surveillance?
At the heart of modern camera systems is a software layer that does more than simply record footage: it also interprets what appears in the image. Video analytics draws conclusions in real time, for example by detecting when someone enters an area, when a vehicle passes through a gate, or when an object is left unattended. The technology complements human monitoring and acts as an automated filter, flagging relevant events for an operator or triggering alerts directly. At larger sites, video analytics has become standard practice, as it is virtually impossible for an operator to monitor dozens or hundreds of cameras simultaneously.
How Video Analytics Interprets Images
The video analytics engine compares each frame against rules or models defined by the administrator or the supplier. Traditional rule-based analytics is configured to detect pixel or object changes within predefined zones, for example, “raise an alarm if anything moves through this polygon between 22:00 and 06:00”. Modern analytics uses machine learning to classify an object first and then apply rules, such as “raise an alarm if a person remains stationary inside the perimeter fence for three minutes”.
Analytics can run in two locations. Edge analytics runs directly on the camera, making it energy-efficient and allowing it to continue working even if the network connection is lost. Server- or cloud-based analytics runs centrally and has access to greater computing power, making it suitable for more advanced models. Many modern systems combine both approaches, allowing the camera to handle basic classification while more computationally intensive analytics runs centrally.
Typical Features
The capabilities of today's systems cover a wide range, from simple rule-based alerts to AI-driven analytics:
- Person and vehicle detection with classification by type
- Line crossing and intrusion detection within defined areas, with time-based rules
- People or vehicle counting for flow analysis
- Detection of abandoned or removed objects
- Machine learning to identify unusual behaviour, such as erratic movement or loitering
- Automatic Number Plate Recognition (ANPR) for vehicle identification
- Face detection, with an important distinction between face detection and facial recognition
- Thermal analytics for challenging lighting conditions
These features can be freely combined and linked to different alarm levels, allowing the same camera to generate both high-priority alerts and log data for subsequent review.
AI, Deep Learning and False Alarms
One recurring problem with earlier generations of video analytics was the number of false alarms. Trees moving in the wind, rain, shadows and small animals could trigger alerts that operators had to filter out manually. Modern systems use neural networks trained on large datasets to distinguish a person from a rabbit or a lorry from a bush, significantly reducing the false alarm rate.
AI also enables new types of analytics that were previously impractical, such as detecting specific behaviours, correlating events across multiple cameras and generating predictive alerts. However, the technology still requires training and maintenance, as environments change over time and new scenarios may require recalibration.
Applications
Video analytics has made the greatest difference in the following areas:
- Perimeter protection for industrial sites, ports and critical infrastructure
- Retail environments for visitor counting, shoplifting detection and queue monitoring
- Traffic monitoring using ANPR for tolling systems and alerts
- Hospitals and healthcare facilities for fall detection and the identification of unusual behaviour
- Logistics and warehouses for security and process optimisation
- Schools and public buildings for the early detection of disturbances
What these applications have in common is that video analytics reduces the burden on staff and shifts the focus from passive monitoring to responding to relevant events.
Privacy and Legislation
The rules governing video surveillance and video analytics vary from country to country. The use of facial recognition and the processing of biometric personal data may be subject to specific restrictions and additional requirements. Organisations should therefore check which laws, regulations and data protection requirements apply in the country where the system is to be used before implementing the technology.
Implementation and Management
Before implementing video analytics, an organisation should clearly define the specific events the system is expected to detect and how alerts should be handled for each type of detection. Without clearly defined scenarios, there is a significant risk that the analytics will generate noise rather than value.
Specify requirements for false alarm rates, classification accuracy and how the supplier updates its models over time. Also set requirements for privacy-enhancing features, how log data is stored and how easily footage can be exported for use in police reports. A long-term management perspective is essential to ensure that the analytics continues to perform effectively over time.
Establish procedures for regularly reviewing alarm statistics, adjusting detection zones and training the staff responsible for responding to alerts. This helps ensure that the technology leads to action rather than simply generating logs.
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