Computer vision · Video analytics
Banao builds video analytics that watch your camera feeds while they are live: counting people, tracking objects across frames, detecting unsafe events, and sending an alert the moment a threshold is crossed — not after a supervisor reviews twelve hours of footage.
We deliver the full system: stream ingestion, detection models, alert logic, dashboards, and the monitoring that keeps accuracy from sliding as cameras age, lighting changes, and crowds grow or thin. CP Plus is a client we work with in this space.
CP Plus— a client Banao works with in the video intelligence space.
Video analytics is not one model on one camera. It is the stream, the detection layer, the alert logic, the integrations, and the monitoring that keeps it working across a site and a year.
Models that detect and follow people, vehicles, and objects across frames — assigning identities across camera cuts so you can trace a path through a site, not just flag a single moment.
Accurate headcounts and flow rates for concourses, retail floors, and factory gates — useful for occupancy limits, queue management, and staffing decisions that need numbers, not estimates.
Models trained to recognise defined events — a person loitering, a vehicle in a restricted zone, a worker without PPE, a queue forming — and fire an alert when the event is confirmed rather than just glimpsed.
Virtual tripwires and zone rules that trigger when an object enters, exits, or dwells in a defined area — without covering every pixel of footage or generating an alert for every incidental movement.
Watching a factory or warehouse floor for PPE non-compliance and unsafe proximity to machinery, with an alert that reaches the floor supervisor before the hazard becomes an incident.
Counting customers by zone, measuring dwell time at fixtures, and tracking queue depth at checkouts — from cameras already on-site, producing the numbers a merchandising or operations team can act on.
Detection is the start; the alert has to reach the right person, with context, fast enough to act. We build the path from model verdict to SMS, email, or integration with your existing VMS or PSIM.
A single dashboard across dozens of cameras and multiple sites, with consistent detection logic and a centralised alert feed — so a security or operations team can manage the whole estate from one screen.
A live camera stream is 25 frames per second of raw image data. Getting from that to an alert a supervisor can act on in 30 seconds involves stream ingestion, per-frame detection, multi-frame tracking, event confirmation, false-positive filtering, and routing to the right channel — all in the time it takes the next frames to arrive.
We build the full path, not just the model. Stream handling, the detection and tracking loop, the event-confirmation logic that waits for a behaviour to be established before it fires, the suppression rules that stop the same event triggering twenty alerts, and the delivery mechanism that gets the right notification to the right person. The model is a week's work; the path it sits inside is the project.
A model that fires on the first frame a person appears will bury a security team in noise. We build a confirmation window — the person must be in the zone for N seconds, or the event must appear in M of the last N frames — before an alert fires.
More detections do not mean more useful alerts. We tune suppression rules, cooldown periods, and severity tiers so a security or operations team gets alerts they act on, not a feed they learn to ignore.
A safety alert that needs to stop a machine must be generated in milliseconds, on edge hardware at the camera. A retail analytics report can wait. We choose the right processing location for the latency the use case demands.
When a camera feed drops or a model scores below its confidence threshold, the system escalates to a human reviewer rather than silently passing the window. A camera that stopped working is flagged, not ignored.
Video analytics projects fail on operations more often than on technology. The pilot ran on a tidy room with consistent lighting; the production cameras are mounted on warehouse ceilings, pointed into variable sun, with a dozen workers crossing the frame at once. The gap between those two environments is where most deployments die.
We name these failure modes on the first call, because each one is a project decision, not a model limitation.
A system that fires fifty alerts per shift teaches its operators to treat them as background noise. The right false-alarm rate is the one the operations team will act on; we tune to that bar, not to a detection-rate number.
A low-resolution camera mounted too high, aimed into direct sun, with motion blur — no model survives this. We audit camera placement and lighting before we quote, and recommend changes before we build.
A detection that sits in a dashboard nobody checks is not an alert. We build the integration to the channel that reaches the right person — radio, SMS, VMS, or the screen they already watch.
Summer light is different from winter light. A new shift uniform changes how a clothing-colour rule fires. Without monitoring and a retraining path, the system that passed acceptance quietly degrades until someone notices the incidents it stopped catching.
Metrics shown dotted (··) are being finalised in our case-study metrics pack and published only once verified. The deployments are real.
CP Plus
We work with CP Plus in the video intelligence space, building analytics on top of their camera infrastructure to extract events and counts from live feeds that would otherwise require manual monitoring.
Industrial facility (anonymized)
We built video analytics watching a production floor for PPE non-compliance and unsafe proximity events, routing confirmed alerts to floor supervisors before the situation could escalate. The system runs across multiple cameras, edge-deployed at the facility.
Retail chain (anonymized)
We extracted zone-level foot-traffic counts and queue depth readings from a retail chain's existing CCTV network, replacing a manual counting process and giving operations a live view of which checkouts and zones needed staff.
Banao runs AI in production across its own ~300-person engineering company every week. InterviewGod screens our own hires. Vikaas runs our own demand generation. Neither is a camera system — but both share the discipline that makes video analytics survive beyond the pilot: measured baselines, monitored drift, a human path for the cases the model cannot resolve, and a team that would notice if accuracy started sliding.
That discipline is what we bring to every video analytics build. The difference between a pilot that impressed and a system that runs reliably on the third shift is the same standard we apply to the AI our own company depends on.
We have seen video analytics applied to problems it cannot solve. We will say so before a budget is spent:
We don't quote a build from a brief. We test whether detection works on the specific event and camera setup that matters most to you.
We audit your camera infrastructure, test detection on your hardest event using samples from your actual feeds, and hand back a feasibility verdict, a system architecture, and ROI maths — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
We develop the detection models, build the stream ingestion and alert pipeline, integrate with your VMS or notification channels, and hold the system to your acceptance criteria before go-live.
We deploy with dashboards on detection accuracy and alert volumes, a human-review path for low-confidence events, and a retraining loop that keeps the system accurate as your environment changes.
What is video analytics?
Video analytics is the use of computer vision models to automatically detect events, count objects, and extract information from camera feeds — in real time or from recordings — instead of relying on a person to watch or review footage manually.
What events and behaviours can video analytics detect?
Intrusion and perimeter breaches, loitering, crowd counts and flow rates, queue depth, PPE and safety violations, vehicle counting and tracking, object presence and removal, and zone occupancy. Which events the system can reliably detect depends on camera quality, event frequency, and the volume of labeled training examples available.
How do you reduce false alarms in production?
We build a confirmation window so an event must persist for a defined period before an alert fires, tune confidence thresholds to your actual tolerance, add suppression rules to stop the same event triggering repeatedly, and set severity tiers so high-confidence alerts get a different response than borderline ones. Alert fatigue is a project risk we engineer against from the start.
Can video analytics work with our existing cameras and VMS?
In most cases, yes. We ingest streams from standard RTSP-capable cameras and integrate with common VMS platforms. Where existing cameras are unsuitable — wrong resolution, angle, or lens — we flag this during the Discovery Sprint before the build starts, so you know what infrastructure changes are needed and can plan for them.
Does the system process video at the camera or in the cloud?
It depends on the latency requirement. Safety alerts that need to stop a machine or dispatch a guard must be generated at the edge, inside the facility, in milliseconds. Retail analytics feeding an end-of-day report can run in the cloud. Most deployments are hybrid: edge detection for real-time alerts, cloud aggregation for reporting and trend analysis.
Does our video data stay in our region?
Yes, where your policy or regulation requires it. We build so video streams and extracted events stay inside your region — UAE, Saudi Arabia, UK, US, or India — with the audit trail your compliance team needs. For GCC clients that means UAE PDPL and Saudi SDAIA alignment; for UK clients, UK GDPR; for India, the DPDP Act.
How do you stop accuracy from drifting over months?
We monitor alert volume, detection rate on a held-out clip set, and confidence distribution, and alert when any metric moves outside the baseline. Seasonal lighting changes, new staff uniforms, or a camera replacement all shift the image distribution the model was trained on. A retraining path built in from the start is what keeps launch-day accuracy running six months later.
How long does it take to build and deploy a video analytics system?
A common path is a 2-week Discovery Sprint to audit cameras and test feasibility, then a build of 6–10 weeks depending on the number of event types, cameras, and integrations required. Banao's engineering bench in Bangalore and Chandigarh means work starts in weeks, not months.
Whether it's a safety violation on the night shift, a queue that builds before anyone notices, or perimeter activity nobody monitors live — in 45 minutes we will tell you whether video analytics can detect it reliably, and what building the system would take.
Book a 45-min scoping call
