Why businesses are using AI false alarm filtering

What is false alarm filtering?

False alarm filtering involves the use of smart technologies, informed installation practices and thorough staff training to help stop innocuous events from activating security systems.

Research suggests that as many as 98% of security alarms can be considered false alerts, with these events costing building owners more than $100 million to address in an average year. 

By implementing dedicated technologies and organizational practices designed to separate false alarms from genuine security threats, building owners, property managers and security teams can reduce alert fatigue, limit resource waste and better protect community members.

How does false alarm filtering work?

Modern false alarm filtering uses AI-powered video analytics to identify genuine threats and innocuous events in real-time video footage. It can accurately and autonomously distinguish between real threats like suspicious vehicles or unknown persons and accidental false alerts like swaying trees or animals and will only alert CCTV operators when action is needed.

A typical AI false alarm filtering workflow will involve:

• Classification: Video analytics software analyzes real-time footage and reference data to identify the type of object or event that triggered the security alert.
• Contextual analysis: The AI false alarm filtering will compare the movement of the subject to wider data to distinguish expected activity from suspicious behavior.
• Scheduling: It will reference user-specified zoning and scheduling rules to see if security events have occurred in high-risk areas and at concerning times of day.
• Verification: The false alarm filtering will check data from wider devices like motion and sound sensors before sending an informative alert to security personnel.
• Learning: Modern AI false alarm filtering solutions will often utilize machine learning technologies to gradually learn site-specific behavior and improve detection accuracy.

By not only spotting suspicious events but also using wider data to understand relevant context, AI-powered false alarm filtering has been shown to reduce false alarms by as much as 95%.

What are the main causes of false alarm events?

Most false alarm events are caused by legacy security solutions mistaking non-threatening events for genuine security incidents. Many traditional security technologies are designed to spot changes in detection stimuli like motion or sound, but cannot identify the cause of the change, leading to false alarms triggered by innocuous events like trees swaying in the wind.

Common causes of false alarms include:

• Animal activity: Pets, rodents, birds and insects passing through target areas can activate motion sensors and cause security cameras to send false alerts to operators.
• Lighting fluctuations: Normal events like cloud cover moving, doors opening, lights flickering and reflections from car headlights can unintentionally trigger security alerts.
• Innocuous motion: Shadows moving across detection areas, trees swaying in the breeze and other forms of innocuous motion can be misinterpreted as safety threats.
• Interference: Some false alarms can stem from equipment malfunctions caused by electrical interference; common sources include nearby power lines and transformers.
• Human error: Failure to adjust security systems to suit novel conditions like storms, unique events and extended business hours can cause accidental false alarm events.

For high-traffic businesses and organizations with limited resources, these false alarm events can quickly lead to alert fatigue. If security staff are faced with endless streams of data that often turn out to be false alerts, leaders run the risk of genuine security threats being missed.

How modern security improves false alarm reduction

Modern security uses smart, AI-powered security technologies to reliably identify the cause of activation events and filter out non-emergency incidents. This helps to improve false alarm reduction by ensuring operators are only asked to respond to genuine safety threats.

Traditional security identifies threats by measuring basic environmental changes. For example, if a certain number of pixels in a video feed change during a set period of time, the system will register this as a potential threat and send a standard alert to security personnel.

Modern security with AI-powered security cameras and intelligent false alarm filtering features analyzes the content of security data to understand what caused the event. The system can reference training data and user rulesets to detect specific objects and anomalous activity, helping to filter out non-emergency events and ensure real alarms contain the necessary context to inform an effective response.

AI false alarm filtering solutions can also learn expected behaviors over time, enabling teams to navigate site-specific environmental conditions. Leaders can choose which types of events should trigger alerts and which should simply be logged for later review, helping operators to not only prevent false alarms but also freely adjust security solutions to meet changing demands.

Impactful use cases for false alarm filtering software

False alarm filtering software can offer significant benefits to all kinds of modern businesses and organizations. In any environment where security systems are in operation, false alarms can lead to resource waste and poor safety awareness, leaving facilities vulnerable to attack.

Below are some examples of use cases where false alarm filtering can be particularly useful.

Perimeter protection

AI-powered false alarm filtering can help security staff to improve intrusion detection around facility perimeters. Systems can be programmed to identify suspicious events like individuals scaling fences or unknown vehicles parked on access roads, while filtering out expected and innocuous events like authorized persons walking through entry gates during business hours. 

Commercial building security 

Security observation at commercial properties can be difficult as multiple employees, guests, contractors and customers require access to specific areas at different times. AI false alarm filtering can be trained to discern normal activities associated with different areas of the property at specific times to help ensure only anomalous incidents trigger commercial security system alerts.

Critical infrastructure security 

Minimizing false alerts and determining the context behind security events is vitally important when safeguarding substations, gas pipelines, airports and other critical infrastructure. Modern false alarm filtering can help to both tackle alert fatigue and ensure CCTV operators are informed of key insights like the presence of weapons during an event to guide swift and safe responses.

Industrial workplace safety

AI-powered false alarm filtering solutions can help to improve both security responses and workplace safety in industrial environments. In addition to detecting security threats, systems can be trained to tell the difference between normal stimuli like steam and dust and signs of a fire like smoke and embers to help reduce false alarms for fires and other industrial hazards.

Healthcare and school security

False alarm filtering can help healthcare and school security staff make efficient use of finite resources by preventing false alarms for normal activities in high-traffic environments. Teams can train systems to understand expected events and detect common threats like aggressive behavior, persons holding weapons and intrusion events to safeguard vulnerable individuals.

Best practices for improving false alarm detection

Improving false alarm detection requires a combination of smart technologies, well-informed installation efforts and ongoing organizational best practices to help ensure security systems function effectively and on-site teams understand how to navigate installations appropriately.

To improve false alarm performance, business owners and leaders should:

• Consider positioning: False alarms can be triggered by a wide range of stimuli from lights and motion to heat and sound; try to position IP cameras and sensors away from common triggers of false alerts like heat sources, soft furnishings and moving objects.
• Create arming policies: Creating unique arming policies for certain times of day and workflows can help to reduce false alarms triggered by normal activities; AI false alert filtering solutions will often do this automatically using machine learning technologies.
• Perform routine maintenance: Damaged hardware, software malfunctions, dirt and wildlife interference (e.g., cobwebs or bird nests) can trigger false alerts; ensure sensors and cameras are routinely checked, cleaned and updated to help prevent mistriggers.
• Pursue integrations: False alarm detection is made much easier when systems and operators can review multiple data sources to understand activation events; integrate CCTV technology, sensors and access control to capture security data across multiple platforms.
• Conduct training sessions: Over 80% of false alarms involve an element of human error; conduct regular training sessions with all employees and regular service users to help ensure all personnel are using and navigating security approaches appropriately.
• Regularly assess solutions: Security and operational practices can always be optimized to improve responses and reduce false alarms; regularly review security reports, test devices and assess solutions to continuously improve security outcomes.

Help reduce false alarms with Calipsa Pro Analytics

Reducing false alarms must be a top priority for all modern organizations. Well-implemented false alarm filtering techniques and solutions can help professionals of all skill levels improve incident responses and avoid alert fatigue, enabling leaders to best address complex threats.

Calipsa Pro Analytics is a practical AI false alarm filtering solution that can be integrated into existing smart cameras and used to reduce false alarms by as much as 90%. Add intelligent video analytics to your security today and empower your teams to act on quality insights.