Advanced AI Video Analytics

Hypersensor is Flame's proprietary technology that transforms existing security cameras into high-performance analytical sensors through AI software. Unlike dedicated counting sensors (infrared, thermal, stereoscopic) that require specific hardware per counting point at EUR 500-2,000 per unit, Hypersensor works with standard IP cameras and people counters that most venues already have installed. The AI processes video streams to extract multiple metrics simultaneously: People Counting, flow analysis, dwell time, and Zones analytics—all from a single camera.

Hypersensor reaches over 99% accuracy in People Counting, matching or surpassing the best dedicated stereoscopic sensors and significantly outperforming infrared (85-90%) and thermal (80-90%). The key is the deep learning model trained on millions of real-world scenarios—from crowded shopping malls to physical spaces and stores—adapting to the specific conditions of each installation. Hypersensor handles complex scenarios that cause errors in conventional sensors: compact groups, people with large bags, baby strollers, wheelchairs, and counter-flows in narrow entrances. Accuracy is validated through quarterly field audits at client locations, including leading retailers, shopping mall operators, and organizations managing physical spaces.

Yes. Hypersensor includes a staff exclusion module that learns to identify typical employee movement patterns—restricted-access zones, repetitive movements, extended presence in work areas—and automatically excludes them from visitor counts. This is not based on facial recognition or personal identification, but on analyzing behavior patterns by zone. Alternatively, exclusion zones (warehouses, staff areas) can be defined where all movement is discounted. For organizations with variable or large teams—whether they manage physical spaces, store chains, or shopping malls—the combination of zone-based and behavior-based exclusion delivers the most reliable results.

Hypersensor scales with no practical camera limits. The architecture is distributed: each camera is processed as an independent stream, allowing cameras to be added incrementally without affecting system performance. In production, Flame simultaneously manages thousands of camera streams across its client network. For an individual deployment, sizing adapts to needs: from 2-4 cameras in a small store to more than 100 cameras in a large shopping mall. Cloud processing scales automatically. For clients who prefer on-site processing (edge computing), Flame provides processing devices that handle 8-16 cameras per unit, stackable as needed.