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Omnilert Unveils Third Generation of its AI Visual Gun Detection System, Increasing Technology Leadership on Performance, Accuracy and Ease of Use


Delivers Significant Enhancements in Hardware Efficiency, Enterprise Management and Gun Detection Accuracy to Better Save Lives in an Active Shooter Situation 

LEESBURG, VA – February 21, 2024 – Omnilert, a leader in active shooter solutions, today announced the release of its third generation AI visual gun detection system, featuring significant advancements designed to further advance the industry’s most effective gun detection system. As the only solution to deliver a unique combination of detection, verification, activation and notification, Omnilert Gun Detect now protects hundreds of schools, universities, hospitals, retail and commercial buildings, and other organization facilities and campuses around the country. The enhancements made in this new release increase the company’s technology leadership with hardware efficiency, enterprise management and gun detection accuracy.

“Today’s customers better understand the technology behind AI visual gun detection and they want more than just detection and verification; they are also concerned with the accuracy of the models and how they are trained, as well as the capabilities the system can provide throughout an entire active shooting incident,” said Dave Fraser, CEO of Omnilert. “This new product release further boosts the core capabilities of Omnilert Gun Detect to empower customers with a layer of technology that can detect weapons with speed and accuracy, followed by a comprehensive and automatic response.”

“We can’t respond our way out of these active shooter tragedies,” said Chris Grollnek, Founder and Managing Principal of the Active Shooter Prevention Project. “Our first line of defense should be prevention and with Omnilert’s ability to instantly identify a weapon either inside or outside a facility, this can provide first responders with the gift of time they need before shots are fired to ensure the best possible outcomes.”

Omnilert Gun Detect is an AI-powered visual gun detection software that delivers reliable, 24/7 monitoring of gun threats using existing security cameras. The system can identify a weapon in a fraction of a second, Omnilert Monitoring can verify in as little as three seconds and then activate a response in only two more seconds that can include dispatching police, locking doors, sounding alarms and automating other responses to notify those in harm’s way and save lives. One of the unique capabilities of the system is that it sends both still images and a short video clip when a threat is detected to provide more context to security personnel and first responders. During an event, it can also deliver ongoing situational awareness about the location and details of the shooter so important decisions can be made based on the timeliest data.

Enhancements to Omnilert Gun Detect

With the third generation of this system, Omnilert has added several key enhancements that provide customers effective, efficient and accurate AI visual gun detection technology that can be easily added as a vital layer of their overall security efforts.

Superior Hardware Efficiency
Omnilert Gun Detect’s AI architecture has been optimized to boost performance 10X for any given GPU over its original architecture, enabling more camera streams to be processed per server. Benefits of these enhancements include:

  • Lower hardware costs for customers.
  • High performance processing on highly available, commodity GPU and server platforms.
  • (55) 1080P camera streams running at 15fps is achievable on a standard NVIDIA RTX 4060.
  • Architecture efficiency for ensuring high frame rate video processing, providing 3-5X more opportunities to detect a gun every second vs. other solutions on the market.
  • System operations occur at the provided frame rate in real time, instantly processing Variable Frame Rate streams from 1FPS to 30FPS without any delay in detection capability.
  • PTZ (pan, tilt, zoom) camera compatibility due to real time at frame rate analysis.

Better Enterprise Management
New clustering enhancements to Omnilert Gun Detect ensure enterprise deployment to tens of thousands of cameras across thousands of sites and servers manageable from a central console. Benefits of this include:

  • Simplified management of large deployments across school districts, healthcare systems and throughout entire retail, commercial, government and corporate enterprises.
  • Easily allows for centralized, dense rack server deployments in data centers to decentralized site-level server or appliance installations or a combination to accommodate a variety of networking topologies.

Improved Detection Accuracy

This is just one example of Omnilert’s organic training data from a busy street in Tokyo, Japan. True, organic data is captured directly from video cameras in various real-world settings such as a schools, hospitals, and busy public environments.

The Omnilert Gun Detect neural network model is in its 38th revision, with continual learning and refinements from curated organic video through real world customer deployments over its five years of development and fine tuning. This accuracy is unmatched in the industry compared to solutions created from image datasets that rely on synthetic/green screens or emulated data for training which produces artifacts not found in real life. This approach reduces the system’s ability to be tuned or discern the difference between objects, resulting in higher false positives, more noise and significant operational burden irrespective of who is performing the verification function.  In contrast, Omnilert’s proprietary data centric AI model does not simply compare objects to a database of known gun images. It is based on raw video footage captured directly from security cameras in various real-life settings around the world and reflects the genuine complexities and nuances of everyday environments such as changing lighting conditions to specific human behaviors. This ensures higher detection accuracy and efficiency in processing which translates into lower hardware requirements.