CCTV Monitoring Agent

A CCTV monitoring agent watches your existing camera feeds continuously and alerts a human when something you have defined happens — someone in a restricted area, a person on the floor, a worker without a helmet, a crowd building at a gate. It uses the cameras you already own and turns recorded footage into something that acts in time to matter.

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Who it's for

Warehouses, societies, retail, industrial sites.

What changes

You find out during the incident, not the next morning.

Starting at: ₹1,50,000
Timeline: 6–12 weeks
Category: AI Vision Systems
Built from: Vashi, Navi Mumbai

Key takeaways

Recorded CCTV is evidence after the event; monitoring is prevention during it.
One guard cannot usefully watch more than 6–8 screens; software watches all of them.
False alerts are the failure mode that kills these deployments, not missed events.
Runs on your existing cameras via RTSP in most cases — new hardware is often unnecessary.
Deploys in 4–8 weeks from ₹3,00,000 for a 16-camera site.

What you are actually buying

Most sites already have cameras and a recorder. What they do not have is anyone watching. The footage gets reviewed after a theft, an accident or a dispute, which is useful for insurance and useless for prevention.

The agent watches every feed, every second, and raises a specific alert to a specific person when a defined condition occurs. The value is entirely in the gap between the event and the response.

This is not general intelligence about your premises. It detects the things you asked it to detect, which means the definition work at the start determines whether the system is worth anything.

Detections that work reliably in the field

Reliability varies considerably by detection type. This is what we have found holds up in Indian industrial and commercial sites.

Detection	Reliability	Common false trigger
Person in restricted zone	High	Authorised staff, solved by schedule rules
Perimeter crossing after hours	High	Stray dogs and cattle
Person on the ground (fall)	Moderate to high	Sitting, crouching to work
Missing helmet or vest	High in good light	Distant or partially occluded workers
Crowd density threshold	High	Shift change, needs time rules
Loitering beyond N minutes	Moderate	Staff on a break
Fire or smoke	Moderate	Steam, dust, welding
Vehicle in wrong lane	High	None significant

Working with the cameras you have

Nearly all IP cameras and NVRs installed in the last decade expose an RTSP stream, which is all the agent needs. Analogue cameras through a DVR usually work too if the DVR has a network output.

Where existing cameras genuinely fall short is resolution and placement. A camera at 720p mounted twelve feet up looking down a corridor cannot reliably tell whether a person is wearing a helmet. We assess each feed during the survey and tell you which ones are usable for which detections, rather than promising everything on everything.

Processing runs on a local machine at the site. Video does not leave your premises, which keeps bandwidth costs at zero and avoids a separate conversation about footage in the cloud.

How alerts should reach people

An alert nobody sees is not an alert. The routing design matters as much as the detection.

WhatsApp with the frame attached. The supervisor sees what happened, not a code, and can judge in two seconds.
Escalation on no response. Unacknowledged within five minutes, it goes to the next person up.
Different routes by severity. A fall alerts the safety officer immediately; a loitering alert goes to a review queue.
Schedule awareness. Nobody wants a perimeter alert at shift change when fifty people cross the line legitimately.
A daily digest. What was detected, what was acknowledged, what was ignored — which is how you tune the system over time.

Safety compliance as the strongest use case

Of everything we deploy in this category, PPE compliance monitoring produces the clearest return. It is objective, it is continuous, and the consequence of failure is severe.

The system checks that anyone in a designated zone is wearing what they should be — helmet, vest, sometimes gloves or eye protection — and flags exceptions. Over a few weeks the aggregate data is more valuable than the individual alerts: which zones, which shifts, which times of day compliance drops.

Used as a coaching tool with supervisors it works well. Used to fine individual workers it produces resentment and creative avoidance of camera angles, which is the same adoption failure that affects every monitoring system.

Scope, cost and rollout

Four to eight weeks for a 16-camera site: survey, edge hardware, feed integration, zone and rule definition, the silent tuning period, then live alerting.

From ₹3,00,000 for that scale including the edge machine, with cost driven mainly by camera count and how many distinct detections you need. More cameras need more compute; more detection types need more configuration and tuning time.

You receive the on-premise system, the rule configuration interface, the alert routing, a review dashboard, and full source. Rules are yours to change — zones move, shifts change, and a system that needs us for every adjustment will drift out of date.

FAQ

CCTV Monitoring Agent — your questions

How many cameras can one machine handle?

A mid-range edge machine with a GPU handles roughly 12 to 20 feeds depending on detection complexity and frame rate. Most detections do not need 25 frames a second — running at 5 fps triples capacity with no practical loss for things like PPE or zone intrusion. Fall detection and fast-motion events need higher rates. We size the hardware against your specific mix rather than a camera count, and larger sites simply get a second machine.

Does it record footage as well?

No, and it should not. Your existing NVR handles recording and retention, which it already does adequately. The agent reads the live stream, analyses it and stores only the frames attached to alerts. Keeping those two systems separate means your recording continues untouched if the analytics machine is down, and it avoids duplicating storage that is already paid for.

Can it identify specific people?

Technically yes, but that is a different system with different obligations. Adding face recognition to surveillance cameras moves you into biometric data processing under the DPDP Act, with consent requirements that are difficult to satisfy for visitors and members of the public. Most sites do not need it — detecting that a person is in a restricted zone is actionable without knowing who they are. Where identification is genuinely required, we treat it as a separate, explicitly consented deployment.

What about privacy for our own staff?

Monitoring in workplaces is generally lawful where employees are informed, the purpose is legitimate and the coverage is proportionate. What that means practically: signage at monitored areas, no cameras in changing rooms, canteens or toilets, a written policy staff have seen, and detection rules aimed at safety and security rather than productivity surveillance. We will build what is asked for, but we advise against rules that measure how long individuals spend away from a workstation — they generate disputes and rarely survive scrutiny.

What happens at night or in poor light?

Detection quality follows the camera. Infrared cameras give usable monochrome images and person detection works well on them; colour-dependent detections like vest compliance do not. Areas that matter at night should have either IR cameras or adequate lighting, which is often the cheaper fix. We map night-time capability per feed during the survey so you know before deployment which detections are daytime-only.

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