An operator begins an overnight shift responsible for cameras across several locations.

During the first hour, the video monitoring system generates alerts for a delivery driver, a resident walking a dog, an employee taking out the garbage, headlights crossing a detection zone and a person passing outside the property line.

Every alert is reviewed.

Every alert is harmless.

The same pattern continues throughout the night. Human detection boxes appear. Notifications enter the queue. Operators open video clips, review the activity and close each event without taking action.

Then, at 2:37 a.m., another person approaches a construction site.

The detection looks almost identical to hundreds of previous alerts. The individual is not running. No weapon is visible. Nothing about the first few seconds clearly announces an emergency.

But this person is watching passing vehicles, checking the fence and waiting for an opportunity to enter.

The camera detected a human.

The real question is whether the security operation helped the operator recognize the threat.

This is where false alarms become more than an inconvenience. A system that repeatedly presents harmless, duplicate or irrelevant events can gradually influence what operators expect, what they notice and how seriously they treat the next alert.

False alarms do not simply waste time.

They can train operators to expect that nothing is wrong.

What Is Alarm Fatigue in Video Monitoring?

Alarm fatigue occurs when people are repeatedly exposed to alarms or alerts that are false, irrelevant, repetitive or unlikely to require action. Over time, they may become desensitized, respond more slowly or begin treating new alerts as routine.

Most formal alarm-fatigue research has been conducted in healthcare rather than physical security. However, the underlying human-factors problem is highly relevant to remote video monitoring and security operations centres: when important signals are buried among large volumes of non-actionable alerts, people can become less responsive to the signals that matter.

For guards and video monitoring operators, alarm fatigue may be caused by:

• Excessive human or vehicle detections

• Repeated alerts for the same person

• Animals, shadows, weather or moving vegetation

• Activity outside the actual protected area

• Authorized employees triggering after-hours alarms

• Poorly positioned cameras

• Incorrect detection zones

• Multiple cameras reporting the same event independently

• Alerts that technically satisfy a rule but require no operational response

• Systems that cannot distinguish expected activity from suspicious behaviour

The psychological cost of these alerts is easy to overlook because it does not always appear in operational reports.

A dashboard may show that every event was reviewed.

It does not show whether the operator approached the five-hundredth event with the same attention as the first.

Why Do People Stop Reacting to Repetitive Alerts?

The human brain cannot give equal attention to every repeated stimulus indefinitely.

When a sound, image or event occurs again and again without meaningful consequences, the brain gradually reduces its response. Psychologists describe this process as habituation. It helps people filter ordinary background information and reserve attention for change.

Habituation is usually useful.

Someone who works near a busy road eventually stops consciously noticing every passing vehicle. A person working in an office may stop hearing the ventilation system. The stimulus is still present, but the brain no longer treats it as new or important.

In video monitoring, the same adaptive mechanism can become a security risk.

The first after-hours human detection at a restricted property may receive careful examination. After dozens of similar detections involving cleaners, residents, employees or pedestrians, the operator begins learning a pattern:

These alerts usually do not require action.

That expectation is not irrational. It is based on experience.

The danger appears when a real threat resembles the harmless events that came before it.

Watching Cameras Is Not the Same as Seeing Threats

Security operators are often described as “watching cameras,” but that phrase significantly understates the work.

An operator may need to determine:

• Whether a person is authorized

• Whether the activity is normal for that location

• Whether the behaviour is changing

• Whether the customer’s security policy has been violated

• Whether another camera provides better evidence

• Whether the person is entering, leaving, lingering or testing access

• Whether a voice warning should be issued

• Whether a guard, supervisor or emergency responder should be contacted

• Whether an escalation could create an unnecessary risk

These are not simple observation tasks. They are repeated judgment calls made under time pressure and incomplete information.

Research on sustained attention shows that performance can decline during tasks that require people to monitor continuously for rare or unpredictable targets. This phenomenon is known as the vigilance decrement. It can involve slower responses, reduced detection performance and increased attention lapses as time on task continues.

This creates a difficult reality for remote video monitoring.

The quieter a site appears, the easier it may seem to monitor. Yet low-activity environments can be especially demanding because operators must remain ready for a meaningful event that may not happen for hours.

A screen showing nothing unusual is not mentally neutral. The operator must repeatedly confirm that nothing unusual is happening.

Every False Alarm Influences the Next Decision

The obvious cost of a false alarm is the time required to review it.

Suppose an operator spends 20 seconds examining an irrelevant event. Multiply that by hundreds of alerts, many operators and multiple customer sites, and the labour cost quickly becomes significant.

But the deeper cost is psychological.

Each harmless event provides another piece of evidence that the next alert will also be harmless.

After enough repetition, the operator may approach new alerts with an understandable assumption:

It is probably another false alarm.

This is sometimes called expectation bias. What people repeatedly experience begins to influence what they expect to see.

The operator may still open the video. They may still follow the required process. But their interpretation can already be leaning toward dismissal before the full event has been examined.

This is why false-alarm reduction is not simply an efficiency project.

It is a threat-recognition and decision-quality issue.

Human Detection Is Not Situational Understanding

Human and object detection can be valuable components of an AI security system.

They can help identify when a person, vehicle or other object enters a camera scene. They can reduce the need for operators to watch every second of live video continuously.

But detecting a person does not explain what that person is doing or why their presence matters.

A human-detection system may identify:

• A resident returning home

• An employee arriving early

• A delivery driver using the wrong entrance

• A contractor working outside the usual schedule

• A person waiting for transportation

• A trespasser checking access points

• Someone attempting to steal equipment

• A person experiencing a medical emergency

• An individual whose behaviour is gradually becoming aggressive

From the perspective of basic object detection, these may all begin as the same event:

Person detected.

Operationally, they are completely different situations.

The same problem applies to vehicle detection. Knowing that a vehicle is present does not reveal whether it is authorized, how long it has remained, whether it is circling the property or whether it is connected to activity on another camera.

This is one of the limitations of deploying detection without enough context.

Before AI, an operator may have been asked to watch too much video.

After AI, the same operator may be asked to review too many detection events.

The interface changes, but the cognitive burden remains.

More Alerts Do Not Automatically Mean Better Security

Security systems are frequently marketed using detection volume:

• More events detected

• Higher sensitivity

• More object classes

• More notifications

• More analytical capabilities

These measurements can be technically meaningful, but they do not necessarily describe the quality of a monitoring operation.

A system that detects every person may create thousands of events without helping operators understand which person requires attention.

A system that sends an alert from every nearby camera may appear responsive while forcing the operator to reconstruct one incident from several disconnected notifications.

A system that identifies movement with high sensitivity may increase detection numbers while reducing operator confidence.

The better operational questions are:

• How many alerts required action?

• How many were duplicates?

• How many were caused by expected activity?

• How many could have been suppressed using site schedules or policies?

• How many alerts arrived faster than operators could review them?

• How often did the system explain why an event was considered important?

• How many real incidents were identified early enough to change the outcome?

Detection rate is a technical metric. Actionability is an operational metric.

A mature AI security monitoring system must consider both.

False Positives Can Change Trust in AI

Operators must develop a working relationship with any automated detection system.

That relationship can fail in two opposite ways.

Operators May Trust the AI Too Much

If an AI security system appears reliable, operators may begin depending heavily on its conclusions.

When no alert is generated, they may assume that nothing important occurred. When an event is labelled low risk, they may give it less attention than it deserves.

This is related to automation bias, where people give excessive weight to an automated system’s recommendation or fail to act when the system omits important information.

NASA research on automated decision support has examined how automation can produce both overreliance and omission errors, particularly when people treat the automated system as a decision-maker rather than one source of information.

Operators May Stop Trusting the AI

The opposite problem occurs when a system repeatedly produces incorrect, vague or low-value alerts.

Operators begin expecting the technology to be wrong.

They may still review the notifications because procedures require it, but the system has lost credibility. A valid alert must now overcome the operator’s previous experience with poor alerts.

Research into human interaction with automation indicates that different patterns of automation error can systematically affect reliance, compliance and trust. A system’s mistakes do not only reduce accuracy; they influence how people respond to its future recommendations.

The goal should not be maximum trust.

The goal should be appropriate trust.

Operators should understand what the system detected, why the event was prioritized, what evidence supports the conclusion and where uncertainty remains.

The Operator Is Not Always the Problem

When an incident is missed, the immediate question is often:

Why did the operator fail to respond?

That may be necessary, but it should not be the only question.

A monitoring failure can result from the way the entire system was designed.

Consider the following conditions:

• The operator received hundreds of non-actionable alerts earlier in the shift.

• The same event generated separate alarms from multiple cameras.

• Site instructions did not clearly define prohibited behaviour.

• The video clip began too late to show how the event started.

• The camera angle did not show the relevant access point.

• The system detected a person but provided no behavioural context.

• The operator was switching between several applications.

• The alert queue was already growing faster than it could be reviewed.

• Previous escalations had been criticized as unnecessary.

• The operator received no feedback about earlier decisions.

• The AI system had produced frequent false positives at that site.

Under these conditions, telling operators to “pay more attention” is not an operational strategy.

It is a way of shifting responsibility from system design to the individual using the system.

Good operators are essential, but even experienced guards and SOC personnel have limited attention. Monitoring technology should respect that limitation rather than constantly testing it.

Operator Fatigue Is Not Only About Being Tired

Night shifts, sleep disruption and long working hours can clearly affect alertness. However, operator fatigue should not be reduced to a sleep problem.

An operator can be fully rested and still become mentally overloaded by:

• High alert volume

• Repetitive review

• Unclear escalation rules

• Constant application switching

• Poor video quality

• Delayed streams

• Inconsistent site instructions

• Duplicate notifications

• Technically correct but operationally useless detections

• The need to verify every AI decision manually

Fatigue can result from the design of the work itself.

A security operation that depends on operators repeatedly correcting or filtering the technology is not truly automated. It has simply moved the work into a different part of the workflow.

AI Should Protect Human Attention

The most valuable role of AI in remote video monitoring is not to generate the greatest possible number of detections.

It is to protect human attention.

That means helping operators understand:

• Whether the activity is expected

• Whether it is happening at an unusual time

• Whether the person entered a restricted area

• Whether the behaviour is continuing or escalating

• Whether the same person appears across multiple cameras

• Whether an event has already been reviewed

• Whether the activity matches a customer-defined security policy

• Whether an immediate response is required

• Whether the system is confident or uncertain

Instead of treating every human or vehicle detection as an independent alarm, an AI security system should help organize video into meaningful events.

For example, a person walking past a construction site should not carry the same operational weight as a person who stops, studies the property, approaches stored equipment and attempts to enter a fenced area.

Both scenarios contain a person.

Only one contains a developing security event.

How Can Security Teams Reduce False Alarms and Operator Fatigue?

Reducing false alarms requires more than increasing the accuracy of an object-detection model.

Remote video monitoring companies, guard providers and SOC teams should examine the complete path from camera to operator response.

1. Measure Actionable Events, Not Raw Detections

Tracking the number of people or vehicles detected may be useful for evaluating system performance, but it does not measure operational value.

Security leaders should also track:

• Percentage of alerts requiring action

• Percentage dismissed as expected activity

• Duplicate-alert rate

• Average operator review time

• Escalation rate by site

• Repeat false-alert causes

• Alerts received per operator per hour

• Incidents detected before loss or damage occurred

• Operator disagreement on similar events

These metrics reveal whether the system is helping or merely generating activity.

2. Add Time, Place and Policy Context

A person entering a warehouse at 2:00 p.m. may be normal.

The same person entering the same area at 2:00 a.m. may require immediate attention.

Context can include:

• Operating hours

• Employee schedules

• Restricted zones

• Direction of travel

• Time spent in an area

• Site-specific rules

• Holiday schedules

• Known delivery periods

• Expected maintenance activities

• Behaviour across multiple cameras

This is the difference between simply detecting objects and supporting security decisions.

3. Consolidate Related Alerts

One person appearing on three cameras should not necessarily create three unrelated tasks.

A modern AI security system should help connect related detections into a single event timeline so the operator can understand how the situation developed.

This reduces repetitive review and improves situational awareness.

4. Remove Predictable Noise

Security teams should regularly investigate why alerts are being dismissed.

Common causes may include:

• Poor detection-zone placement

• Public sidewalks inside monitoring areas

• Reflective surfaces

• Moving vegetation

• Insects close to the lens

• Weather conditions

• Authorized activity outside the configured schedule

• Cameras aimed at irrelevant areas

• Duplicate analytics running on the same stream

False-alarm reduction is not a one-time configuration task. Sites change, customer operations evolve and seasons introduce new sources of noise.

5. Give Operators Clear Instructions

Operators should not have to invent the customer’s security policy during a live event.

Site instructions should clearly explain:

• What activity is permitted

• What behaviour is prohibited

• When two-way audio should be used

• When a guard should be dispatched

• When police or emergency services should be contacted

• Which areas are most critical

• What evidence is required before escalation

• Who should be contacted at different times

Clear policies reduce inconsistent decisions and unnecessary cognitive pressure.

6. Preserve Uncertainty

Security events are not always clearly safe or clearly dangerous.

An AI security system should be able to communicate uncertainty rather than presenting every output as a confident conclusion.

Operators need to know:

• What was detected

• Which part of the scene triggered the alert

• Why the activity may be unusual

• Whether the video is incomplete

• Whether another interpretation is possible

• What additional camera or information may help

Good AI supports judgment. It does not pretend uncertainty has disappeared.

7. Create a Feedback Loop

Operators should learn what happened after an escalation.

Was the person authorized? Did the guard find evidence of intrusion? Did the voice warning stop the activity? Was the alert misclassified? Did the customer’s instructions need to change?

Without feedback, operators cannot calibrate their decisions or their trust in the system.

Feedback also helps monitoring companies identify patterns that technology metrics alone may miss.

8. Calculate Workload Based on Activity, Not Camera Count

One hundred quiet, well-configured cameras may create less work than ten cameras at a highly active site with poor detection zones.

Operator capacity should consider:

• Alert volume

• Site activity

• Policy complexity

• Average review time

• Required documentation

• Number of systems being used

• Escalation responsibilities

• Two-way audio workload

• Frequency of technical problems

• Time-zone and language requirements

Camera count alone is not a reliable measure of operational workload.

What Should an AI Security System Do Differently?

An effective AI security system should not simply place boxes around people and vehicles.

It should help answer the questions an experienced operator would naturally ask:

• Who or what is present?

• Where are they?

• Is their presence expected?

• What are they doing?

• How long have they remained?

• Is the behaviour changing?

• Is another camera showing the same event?

• Does the event violate the site’s policy?

• Is the situation becoming more serious?

• What response is appropriate?

This is the direction ArcadianAI is pursuing with policy-driven AI security monitoring.

ArcadianAI works with existing cameras to help monitoring teams filter repetitive activity, apply site-specific rules, connect related video events and provide operators with more useful context.

The purpose is not to remove human judgment.

It is to reserve human judgment for situations where it can make the greatest difference.

The Future of Video Monitoring Is Not More Alerts

The security industry has spent years improving its ability to detect movement, people, vehicles and objects.

The next challenge is deciding what deserves attention.

A system can be technically accurate and still create a poor operational outcome. It can detect thousands of real people while providing little help in determining which person represents a real threat.

That is why false alarms should not be treated only as a nuisance or labour expense.

They shape operator expectations. They influence trust. They consume attention. They can make genuinely important events feel familiar.

When a threat is missed, the operator may not be the only point of failure.

The alert system may have spent the entire shift teaching that operator that every new notification was probably nothing.

The future of AI security monitoring should not be measured by how often a system says something happened. It should be measured by how reliably it helps people recognize when something truly matters.

Frequently Asked Questions

What is alarm fatigue in video surveillance?

Alarm fatigue in video surveillance occurs when guards or operators receive so many false, repetitive or low-value alerts that they become less responsive to new notifications. It can increase review time, reduce trust in detection systems and make important events more difficult to recognize.

Can false alarms cause operators to miss real threats?

A large volume of false or irrelevant alarms can contribute to desensitization, expectation bias and cognitive overload. This does not mean every missed incident is caused by false alarms, but poorly managed alert volume can make reliable threat recognition more difficult.

What causes false alarms in AI security monitoring?

Common causes include poorly configured detection zones, weather, shadows, animals, moving vegetation, public activity near a property, camera positioning, authorized after-hours activity and systems that detect objects without applying enough site-specific context.

Is human detection the same as threat detection?

No. Human detection identifies that a person is visible in a scene. Threat detection requires additional context, including location, time, behaviour, direction, duration, site policy and whether the activity is expected.

How can remote video monitoring companies reduce operator fatigue?

They can reduce operator fatigue by removing duplicate events, applying site-specific policies, suppressing expected activity, improving camera configuration, clarifying escalation procedures, measuring actionable alerts and giving operators feedback about the outcomes of their decisions.

Should AI replace security operators?

AI is most effective when it supports rather than blindly replaces experienced operators. It can filter repetitive activity, prioritize events and provide context, while people apply judgment to ambiguous or developing situations.

What is the difference between object detection and AI security monitoring?

Object detection identifies visible categories such as people or vehicles. AI security monitoring uses additional information, including schedules, zones, behaviour, event history and security policies, to determine whether that detection may require attention.

How does ArcadianAI reduce false or low-value alerts?

ArcadianAI uses policy-driven video analysis to help distinguish expected activity from potentially significant behaviour. It can apply monitoring schedules, site-specific rules and event context while working with existing camera infrastructure.

Protect Your Operators’ Most Limited Resource: Attention

Your guards and operators should not spend their shifts searching through endless low-value detections for the one event that matters.

ArcadianAI helps monitoring companies, SOC teams and security providers bring greater context, prioritization and consistency to video monitoring without requiring a complete replacement of existing camera infrastructure.

Ready to see how policy-driven AI can support your monitoring operation? Schedule an ArcadianAI demonstration today.