What is Video Analytics?
Video Analytics is the application of AI and computer vision to automatically analyse video feeds, extracting meaningful insights about people, objects, and events in real-time or from recorded footage. It transforms passive surveillance cameras into intelligent monitoring systems that can detect incidents, count visitors, measure dwell time, and trigger automated alerts.
What is Video Analytics?
Video Analytics, also called Video Content Analysis (VCA) or intelligent video analysis, uses artificial intelligence to automatically extract useful information from video streams. Rather than requiring humans to watch hours of footage, video analytics systems continuously monitor camera feeds, recognise patterns, detect events, and generate actionable insights.
Think of it as upgrading a passive recording system into an active monitoring assistant. Instead of reviewing footage after an incident, video analytics can detect events as they happen and alert the right people immediately.
How Video Analytics Works
Video analytics combines several computer vision technologies into an integrated system:
- Object detection: Identifies people, vehicles, objects, and other items of interest in each video frame
- Object tracking: Follows identified objects across frames to understand their movement paths and behaviours
- Activity recognition: Identifies specific actions or events, such as a person entering a restricted area, a vehicle stopping in a no-parking zone, or a crowd forming
- Scene understanding: Interprets the overall context of what is happening in the video, distinguishing normal activity from anomalies
Modern video analytics runs on deep learning models that have been trained on millions of hours of video data. These systems can be deployed on edge devices (processing video locally at the camera) or in the cloud (streaming video to remote servers for analysis), each with different cost, latency, and bandwidth trade-offs.
Types of Video Analytics
Rule-Based Analytics
The system applies predefined rules to detected objects. Examples include:
- Tripwire detection: Alert when someone crosses a virtual line
- Intrusion detection: Alert when someone enters a defined area
- Object removal: Alert when a specific object (like a valuable asset) is removed from a location
- Loitering detection: Alert when someone remains in an area beyond a set time threshold
Statistical Analytics
The system collects quantitative data over time:
- People counting: Track the number of people entering and exiting a space
- Vehicle counting: Count vehicles on roads or in parking facilities
- Queue measurement: Monitor queue lengths and wait times
- Occupancy monitoring: Track real-time occupancy levels in rooms, floors, or buildings
Behavioural Analytics
More advanced systems that understand patterns of behaviour:
- Anomaly detection: Identify unusual activity patterns that deviate from normal baselines
- Crowd behaviour: Detect crowd formation, dispersal, or movement patterns
- Customer journey mapping: Track how individuals move through a space like a retail store
Business Applications of Video Analytics
Retail
Retailers use video analytics extensively to optimise operations and customer experience:
- Footfall counting to measure store traffic and correlate with sales performance
- Heat mapping to understand which areas of the store attract the most attention
- Queue management to dynamically open checkout lanes when wait times exceed thresholds
- Loss prevention to detect suspicious behaviour patterns associated with shoplifting
Manufacturing
Factory environments benefit from video analytics in multiple ways:
- Safety monitoring: Detecting PPE compliance, unsafe behaviours, and restricted area violations
- Process monitoring: Verifying that manufacturing processes are followed correctly
- Equipment monitoring: Detecting abnormal machine behaviour that might indicate impending failures
Transportation and Logistics
- Traffic flow optimisation: Adjusting signal timing based on real-time vehicle counts and queue lengths
- Parking management: Real-time availability tracking and guidance
- Loading dock monitoring: Tracking vehicle arrivals, departures, and dwell times
Hospitality and Tourism
Hotels, resorts, and tourist attractions use video analytics to:
- Monitor occupancy across facilities including pools, restaurants, and fitness centres
- Manage queues at reception, restaurants, and attractions
- Enhance guest safety through real-time monitoring of public areas
Video Analytics in Southeast Asia
Video analytics deployment is accelerating across the region:
- Smart city programmes: Singapore's Smart Nation initiative has deployed video analytics across public spaces for crowd management, traffic optimisation, and public safety. Other ASEAN cities are following, with Bangkok, Kuala Lumpur, and Jakarta investing in intelligent traffic management systems.
- Retail transformation: As Southeast Asian retail modernises, video analytics provides data-driven insights that help physical stores compete with e-commerce. Shopping malls across the region are implementing footfall analytics and heat mapping to optimise tenant mix and layout.
- Tourism management: Popular tourist destinations like Bali, Phuket, and Boracay use video analytics for crowd management, helping balance visitor experience with sustainable tourism practices.
- Infrastructure monitoring: Governments are using video analytics for monitoring critical infrastructure including ports, bridges, and transportation hubs.
Bandwidth and Infrastructure Considerations
A practical consideration for Southeast Asian deployments is internet bandwidth. Video streams consume significant bandwidth, and internet connectivity varies across the region. Edge computing solutions that process video locally and transmit only insights and alerts are often more practical than cloud-dependent architectures, particularly in areas with limited or unreliable connectivity.
Privacy and Compliance
Video analytics systems that process footage of people must comply with data protection regulations:
- Data minimisation: Process the minimum amount of personal data necessary. For many analytics use cases like people counting, individual identification is not needed.
- Retention policies: Define how long raw footage and analytics data are retained
- Access controls: Restrict who can access video footage and analytics outputs
- Privacy notices: Inform individuals that video analytics is in use through clear signage
- Purpose limitation: Use analytics data only for the stated purpose
Getting Started
- Define specific business questions you want video analytics to answer, such as "How many people enter our store hourly?" or "Are workers wearing required PPE?"
- Audit existing camera infrastructure to determine whether current cameras meet the resolution, angle, and placement requirements for analytics
- Choose between edge and cloud processing based on your bandwidth, latency, and infrastructure constraints
- Start with a single use case at one location. Deploy, measure, and learn before scaling.
- Establish clear privacy policies and signage before activating any people-focused analytics
Video analytics transforms one of the most ubiquitous business technologies, security cameras, from passive recording devices into active intelligence systems. For many businesses, the investment in cameras and infrastructure already exists; video analytics unlocks significantly more value from that existing investment.
The operational impact spans multiple dimensions. Security teams become more effective when they receive automated alerts for genuine incidents rather than manually monitoring banks of screens. Operations teams gain data-driven visibility into physical processes like customer flow, queue lengths, and workspace utilisation. Management teams access quantitative metrics for decision-making about store layouts, staffing levels, facility design, and capacity planning.
For businesses operating across Southeast Asia, video analytics addresses a common challenge: gaining consistent operational visibility across geographically dispersed locations. A retail chain with stores across multiple countries can centrally monitor traffic patterns, queue performance, and safety compliance across all locations in real-time, enabling standardised operations and rapid response to issues. This kind of operational intelligence was previously available only to the largest enterprises with dedicated monitoring teams, but cloud-based video analytics platforms have made it accessible to mid-sized businesses as well.
- Start with your existing camera infrastructure. A professional assessment can determine whether your current cameras support analytics, potentially saving significant hardware investment.
- Define specific, measurable business questions you want to answer. Video analytics generates vast amounts of data, and without clear objectives, you risk drowning in metrics that do not drive decisions.
- Evaluate edge versus cloud processing based on your specific infrastructure. Edge processing reduces bandwidth needs but increases per-camera hardware costs; cloud processing is more flexible but requires reliable connectivity.
- Privacy compliance is non-negotiable. Implement privacy-by-design principles, deploy clear signage, and ensure your analytics approach complies with data protection regulations in every market you operate in.
- Integration with existing systems multiplies value. Connect video analytics outputs to your POS system, workforce management platform, or building management system for richer insights.
- Plan for ongoing management. Video analytics systems need regular calibration, rule tuning, and model updates to maintain accuracy as environments and usage patterns change.
Frequently Asked Questions
Can video analytics work with our existing security cameras?
In many cases, yes. Video analytics typically requires cameras that support at least 720p resolution and can provide an IP video stream (RTSP or ONVIF protocol). Most cameras installed in the last five to eight years meet these requirements. However, analytics accuracy depends heavily on camera positioning, field of view, and lighting. A professional assessment of your existing cameras can identify which are suitable for analytics and which may need repositioning or replacement. Software-based analytics platforms that process existing camera feeds are often the most cost-effective starting point.
How much data does video analytics generate and store?
Raw video storage requirements are substantial: a single 1080p camera generates roughly 10-20 GB per day of continuous recording. However, video analytics significantly reduces storage needs by enabling event-based recording (only storing footage when interesting events occur) and metadata-based search (finding specific events without reviewing full footage). The analytics metadata itself, such as people counts, heat maps, and event logs, is very compact, typically megabytes per day per camera. Many businesses find that video analytics actually reduces their total storage costs by enabling smarter recording policies.
More Questions
Traditional motion detection simply identifies that something moved in the camera frame. It cannot distinguish between a person, a vehicle, a shadow, a swaying tree, or a change in lighting, leading to very high false alarm rates, often exceeding 95%. Video analytics uses AI to understand what is happening in the scene. It can distinguish between object types, track specific individuals or vehicles, recognise behaviours, and detect complex events. This typically reduces false alarms by 90% or more while detecting a much wider range of meaningful events.
Need help implementing Video Analytics?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how video analytics fits into your AI roadmap.