Scylla — False Alarm Filtering

False Alarm Filtering for Reliable Video Surveillance

Use Scylla’s powerful AI algorithms to reduce false alarms by up to 99.95%, enhance threat detection accuracy, and optimize security team operations.

How it works:

● Scylla’s False Alarm Filtering detects a person or vehicle in a single frame or sequence of frames.
● Once an object of interest is identified in the frame, an alert is generated and sent to the security personnel.
● Using computer vision and artificial intelligence, Scylla significantly reduces false positives — by up to 99.95%.
● The filtering algorithm can be configured to respond only to specific areas within the frame and during predefined time intervals.

● Intelligent video analytics enhances your hardware with actionable insights, helping security staff eliminate the overwhelming number of false alarms that typically lead to alert fatigue, added costs, and wasted time.● Traditional motion-based surveillance cameras are often ineffective, as they trigger alerts every time something moves in the frame — like a ceiling fan, moving leaves, shifting shadows, or animals. In contrast, Scylla’s AI-powered video analytics is highly discerning and only triggers alerts when a relevant object is detected.● When deployed on-site, Scylla False Alarm Filtering operates 24/7, ensuring continuous situational awareness.

How to distinguish between a true and a false alarm:
An alarm is classified as true when the AI prediction matches reality — for example, when an object of interest is correctly identified or a target action is accurately detected.
A false alarm occurs when an alert is triggered incorrectly. Due to the probabilistic nature of AI, some false alarms are inevitable.

However, with advanced AI and machine learning at the core of the Scylla False Alarm Filtering (FAF) system, it meets — and often exceeds — industrial-grade standards. Additionally, Scylla’s AI modules are constantly improved through retraining on past errors, which allows the system to reduce false alarms even further over time, achieving an impressive 99.5% accuracy rate.

Some vendors claim false alarm accuracy rates of up to 90.00%.
What difference does your solution make?
It’s relatively easy to achieve around 90% accuracy using off-the-shelf deep learning modules. The challenge arises when higher performance is needed.

Let’s consider an example: a monitoring station receives 50,000 events in a given time period from its surveillance network. With a 90% filtering accuracy, 10% — or 5,000 events — still need to be manually reviewed. That’s a massive workload for security operators.

In contrast, with Scylla FAF’s 99%+ effectiveness, under the same conditions, only 1% — or 500 events — remain to be processed.

This difference is critical — it drastically improves operational efficiency, reduces workload, and results in significant cost savings.

Why is an AI-based solution (false alarm filtering) better than human analysis?
The number of surveillance cameras connected to control centers or monitoring stations is growing exponentially. A single operator often monitors events from multiple sites, which means overseeing dozens of camera feeds simultaneously. These cameras can generate dozens of alerts per day.

Without proper filtering and classification, operators either:
– become overwhelmed and miss real threats hidden among numerous false alarms, or
– organizations must hire more staff to handle the growing volume of events, leading to increased costs.

This is where Scylla’s False Alarm Filtering (FAF) becomes essential. It reduces false alarms to near-zero levels, allowing operators to focus only on real, critical alerts. This not only boosts operational efficiency but also significantly cuts operating costs.

Is Scylla GDPR and CCPA compliant?
Absolutely. Scylla does not store any data that could be considered personally identifiable. We do not retain any video recordings or images.

The only data we store are alarm notifications, and their retention period can be adjusted according to the policies defined by the client.

How does Scylla False Alarm Filtering improve operational efficiency and reduce costs?
By leveraging computer vision and artificial intelligence, Scylla False Alarm Filtering supports security teams in their daily operations, enhancing their capabilities while eliminating the overwhelming number of false alarms that typically lead to alert fatigue, wasted time, and unnecessary expenses.

For example:
– A security monitoring company can significantly reduce operator fatigue and labor costs by filtering out false alarms, allowing staff to focus on investigating real threats.
– On a factory site, issues like insects (e.g., moths) triggering perimeter intrusion cameras can be resolved through Scylla's filtering, ensuring operators concentrate only on actual security breaches.

In both cases, Scylla empowers teams to work more effectively, minimizes distractions, and enables smarter resource allocation — ultimately leading to greater productivity and cost savings.

What happens after a threat is detected?
An alert containing all relevant information is generated and sent to the end users responsible for security.

There are several configurable notification channels, including:
– The Scylla Dashboard
– The Scylla Mobile App
– Access point relay boards
– VMS alerting APIs

These flexible options ensure that critical alerts reach the right people in real time, enabling swift response.

Can Scylla False Alarm Filtering work effectively with outdoor surveillance cameras?
Yes, absolutely.
Traditional motion-detection cameras are often unreliable outdoors because they trigger alarms for any movement — like wind-blown leaves, shifting shadows, or passing animals.

Scylla’s AI-powered video analytics, however, understands the cause of motion and triggers alarms only when a selected object of interest is detected within the defined zone.

This makes Scylla highly effective even in outdoor environments, where conventional systems typically struggle.

Can I get a report or analysis of all alarms?
Yes. Scylla provides detailed reports that help analyze alarms and optimize camera placement or security focus in each zone.

Users can filter data by date and access the following statistics for each camera:
– Total number of alarms
– Rejected alarms
– Approved alarms
– Historical timeline within a specified time range

These insights enable data-driven decisions and improve overall surveillance efficiency.

Fill out the form to receive a personalized consultation for your IT infrastructure:

Use Scylla’s powerful AI algorithms to reduce false alarms by up to 99.95%, enhance threat detection accuracy, and optimize security team operations.

How to distinguish between a true and a false alarm:

Some vendors claim false alarm accuracy rates of up to 90.00%.What difference does your solution make?

Why is an AI-based solution (false alarm filtering) better than human analysis?

Is Scylla GDPR and CCPA compliant?

Absolutely. Scylla does not store any data that could be considered personally identifiable. We do not retain any video recordings or images.The only data we store are alarm notifications, and their retention period can be adjusted according to the policies defined by the client.

How does Scylla False Alarm Filtering improve operational efficiency and reduce costs?

What happens after a threat is detected?

Can Scylla False Alarm Filtering work effectively with outdoor surveillance cameras?

Can I get a report or analysis of all alarms?

Some vendors claim false alarm accuracy rates of up to 90.00%.
What difference does your solution make?

Absolutely. Scylla does not store any data that could be considered personally identifiable. We do not retain any video recordings or images.

The only data we store are alarm notifications, and their retention period can be adjusted according to the policies defined by the client.