09/23/2026
Why Too Many Security Camera Alerts Are Becoming a Challenge for Businesses
Introduction
Businesses install security cameras to improve visibility, protect valuable assets, and respond quickly when something unusual happens. From construction sites and warehouses to commercial buildings and parking areas, cameras have become an important part of modern security.
But after installing cameras, many businesses face another challenge: too many alerts.
A camera may send an alert when it detects people, vehicles, moving trees, shadows, animals, or changes in the surrounding environment. Modern video-analytics systems must account for conditions such as poor lighting, fog, heavy rain, snow, insects, shadows, headlights, animals, and moving vegetation because these conditions can affect detection accuracy or contribute to unwanted alerts.
(Source: Axis Communications — Object Analytics)
While the camera may correctly detect movement, not every movement represents a security concern. This can create extra work for security teams. Instead of focusing on important events, employees may spend significant time reviewing alerts that do not require action.
Research into video surveillance has also highlighted the challenge created by the sheer amount of information cameras produce. The National Institute of Justice has noted that surveillance cameras can provide security personnel with an overwhelming volume of video data and that video analytics can help automate the process of finding potentially relevant activity.
(Source: National Institute of Justice — Video Analytics for Criminal Justice Uses)
The challenge is no longer simply having cameras.
Businesses need systems that can help identify which events actually matter.
Did You Know?
Video analytics systems face an important balance between detecting relevant activity and avoiding unnecessary alerts.
The National Institute of Standards and Technology's Activities in Extended Video (ActEV) program evaluates automatic activity-detection technologies for multi-camera environments. The program is designed to measure the performance of systems intended for both forensic investigation and real-time alerting.
NIST's ActEV evaluations use performance measures that include both the probability of missed detection and the rate of false alarms. In other words, a useful surveillance system cannot be evaluated only by whether it detects something. How often it produces incorrect detections also matters.
The problem becomes more significant as the number of cameras increases.
A peer-reviewed study examining CCTV monitoring found that 66% of participants failed to notice a clearly visible unexpected event during a surveillance task. Experience did not completely protect operators from this problem, highlighting the limits of sustained human attention when monitoring video.
(Source: PLOS ONE / PubMed Central)
Research sponsored by the National Institute of Justice has also cited field observations indicating that a single operator may be able to monitor several video screens simultaneously only at a relatively superficial level.
This helps explain why organizations are increasingly using video analytics to help direct human attention toward activity that may actually require investigation.
Why Traditional Motion Detection Creates Too Many Alerts
Many traditional camera systems generate alerts when movement or visual changes occur within a camera's field of view.
But movement alone does not explain what is happening.
For example:
• A person entering a restricted area after business hours is different from someone walking on a public sidewalk.
• A vehicle entering a secured loading area is different from a vehicle simply passing nearby.
• A worker arriving at a construction site during normal working hours is different from an unknown person entering the same site at night.
Basic motion detection may identify all of these situations primarily as movement within the scene.
Environmental conditions make the problem more complicated.
Research into automated visual surveillance has identified changing illumination, background movement caused by wind, and changes in weather as factors that make reliable automated detection more difficult.
(Source: National Institute of Justice — Real Time Computer Surveillance for Crime Detection)
Modern camera analytics documentation identifies similar real-world challenges. Heavy rain and snow can affect detection distance and accuracy, while fog, inadequate lighting, insects, vehicle headlights combined with rain, large animals, and strong shadows can contribute to delayed, missed, or false detections.
For businesses, the goal is not to detect every movement.
The goal is to identify the activity that matters.
Alert Fatigue Can Affect Security Response
Security alerts are useful only when teams can review and respond to them effectively.
When employees receive a large number of notifications, reviewing every event can become time-consuming. Important events may have to compete for attention with routine or irrelevant activity.
This matters because human attention is limited.
Research on CCTV monitoring has demonstrated that even clearly visible events can be overlooked when operators are focused on another monitoring task. In the previously mentioned CCTV experiment, 66% of participants missed an unexpected event, while task-irrelevant events were missed even more frequently.
More recent research has investigated whether AI can help.
A 2023 study published in the International Journal of Industrial Ergonomics tested AI-based visual cueing in a simulated CCTV control room. The researchers found that AI-assisted visual cues could improve monitoring performance and reduce operators' cognitive load.
(Source: De Bruyne et al., 2023)
The wider security industry also demonstrates why unnecessary alarms matter operationally.
For example, research from Arizona State University's Center for Problem-Oriented Policing reported that historically 94–98% of burglar-alarm calls to police were false in the jurisdictions and data it reviewed. This statistic relates to burglar-alarm systems—not security-camera analytics—but it illustrates how poorly filtered security alerts can consume significant response resources.
Similarly, the Urban Institute examined false-alarm programs in Montgomery County, Seattle, and Salt Lake City and reported reductions in false alarms of approximately 66–90% following changes in alarm-management and verification practices. The programs also saved substantial amounts of police time.
The lesson for business surveillance is straightforward: an alert is valuable only when it provides useful information that can support an appropriate response.
Different businesses also face different monitoring requirements:
1. Construction sites: Protection of equipment, materials, access points, and restricted areas.
2. Warehouses: Visibility across large operational and storage spaces.
3. Commercial properties: Monitoring entrances, parking areas, perimeters, and sensitive locations.
4. Remote facilities: Security monitoring where personnel may not continuously watch cameras on-site.
In each case, businesses need better information, not simply more alerts.
AI Video Analytics Adds More Context
Modern AI-powered video analytics can go beyond basic movement detection.
Depending on the technology and how it is configured, AI systems can analyze information about people, vehicles, objects, actions, and activities within video.
NIST's ActEV program, for example, evaluates automatic activity detection in multi-camera video streams and is specifically intended to advance systems capable of identifying meaningful activities for real-time alerting and forensic applications.
Research reviewed by the National Institute of Justice has similarly explored AI systems capable of extracting information about people, objects, and actions from video and images.
A RAND Corporation study on video analytics and sensor fusion also identified the ability to reliably recognize baseline objects, entities, activities, and events as an important foundation for effective video-analytics systems. RAND noted that these technologies are promising but still require continued improvements in reliability, implementation, privacy, and security.
In a business security environment, contextual analytics may help a monitoring system distinguish between situations such as:
• Trees or background objects moving within a scene.
• A vehicle passing outside a monitored area.
• A person entering a restricted zone.
• Activity occurring during non-operational hours.
• Movement that remains within an expected area versus movement crossing a defined boundary.
Modern analytics can also use filters and detection zones to reduce unnecessary activity. For example, some systems allow operators to filter short-lived, swaying, or small objects or establish areas where activity should or should not trigger an event.
The goal of AI should not simply be to generate more alerts.
The goal is to make alerts more meaningful.
By providing additional information about what is occurring within a camera view, video analytics can help human operators focus attention on events that may require further review.
Real-Time Monitoring Helps Businesses Respond Faster
Detecting unusual activity is only one part of security.
The next question is:
What happens after the alert is generated?
Video analytics can help identify potentially important activity, but determining whether an event represents a genuine security concern may still require human judgment.
A National Institute of Justice expert panel examining video analytics noted that automated systems can alert personnel when specified activities or objects are detected while the decision about whether and how to respond remains with human operators.
Academic research supports the potential value of this combined approach. The 2023 CCTV-control-room study found that AI visual assistance helped operators identify relevant information while reducing cognitive load rather than attempting to remove the operator entirely from the decision process.
RAND's research into video analytics also emphasized that the technology remains promising but has not reached a point where every detection or event can simply be treated as unquestionably accurate.
This creates an important difference:
Traditional surveillance:
“What happened?”
Intelligent monitoring:
“What is happening, and does it need attention?”
A stronger security workflow therefore follows a process such as:
Detect → Verify → Respond → Record
Technology can help identify an event, while human judgment helps determine what action is appropriate.
Every Location Needs Different Security Rules
One common challenge with surveillance is applying the same monitoring rules everywhere.
Every environment has its own patterns of normal activity.
• A warehouse operates differently from a parking area.
• A construction site has different security requirements from an office building.
• A loading area experiences different activity from a customer entrance.
For example, a vehicle entering a parking area during business hours may be completely normal. The same vehicle entering a restricted equipment area after hours may require attention.
Similarly, a person entering a building during operating hours may be expected, while someone entering a restricted equipment area late at night may need investigation.
This is one reason modern video analytics provides configurable detection areas, thresholds, filters, and scenarios rather than treating every part of every camera image identically.
Technical guidance for modern object analytics, for example, describes the use of defined detection areas, exclusion zones, object-size filters, and rules for short-lived or swaying objects to reduce unnecessary alarms.
This is why context matters.
Understanding where, when, and what type of activity is expected can help make surveillance alerts more useful.
Security Systems Need Continuous Improvement
Installing a surveillance system should not be the end of the process.
Business environments change.
• Lighting conditions change.
• Landscaping grows.
• Traffic patterns change.
• Operating hours change.
• Construction layouts change.
• Camera positions may shift.
• New buildings or monitored areas may be added.
These changes can affect analytics performance.
Research into automated surveillance has long identified illumination, weather, background movement, and changing scene conditions as factors that can complicate reliable detection.
Current object-analytics documentation continues to identify issues such as insufficient lighting, fog, direct light, heavy rain, snow, camera positioning, contrast, shadows, and environmental movement as factors that can influence detection accuracy.
Businesses should therefore periodically review how their surveillance system is performing and determine whether detection zones, monitoring schedules, filters, camera angles, thresholds, or operating rules need adjustment.
A practical process is:
Review → Identify → Adjust → Monitor
Continuous review helps keep the system aligned with the way the property actually operates.
Why Businesses Choose VERIFAI for Smarter Video Monitoring
Protecting a business requires more than cameras that simply record what happened.
Businesses need visibility into what is happening, which events deserve attention, and when a response may be required.
At VERIFAI, our approach combines AI-powered detection with human verification.
AI helps identify activity across monitored cameras, while trained operators can review detected events and determine whether further action may be required.
This combination helps move surveillance beyond simply recording activity toward a more structured process:
Detect → Verify → Deter → Escalate
Depending on the deployed security solution, response capabilities can include measures such as voice-down communication, sirens, strobes, and two-way audio, allowing monitoring teams to respond remotely when appropriate.
The objective is not simply to send businesses more notifications.
It is to provide more useful security information and a clearer response process.
From construction sites and warehouses to commercial facilities, parking areas, and other business properties, VERIFAI's AI-powered monitoring approach is designed to help organizations improve visibility, verify activity, and respond more effectively to potential security events.
Ready to Reduce Unnecessary Alerts?
Too many notifications can make surveillance harder to manage.
The research around surveillance, human attention, and automated detection points toward an important principle:
More alerts do not automatically mean better security.
A stronger approach combines intelligent detection, contextual rules, human verification, and a defined response process so security teams can focus on events that deserve attention.
VERIFAI helps businesses move from simply recording activity toward understanding, verifying, and responding to what is happening across their properties.
For more information about how IVIS Security can help improve visibility and security across your job sites, visit IVIS Security or call (844) 438-4847.
