The FLIR FH-Series ID combines a full resolution thermal sensor with <30 mK sensitivity and a 4K optical imager into a single camera, enabling critical infrastructure customers to get an assessment of threats at the same time as detection. Bringing both sensors under one housing simplifies installation and reduces infrastructure and project expenses, as only one IP address and cable connection are needed, making the FH-Series ID an attractive option for system integrators.
A key pain point for customers deploying perimeter security cameras is false alarms. Unlike motion-based alarms, which are more susceptible to nuisance alerts, the FH-Series ID addresses this challenge by incorporating FLIR Virtual Barrier video analytics that are based on convolutional neural networks (CNN analytics).
AI-driven with built-in Virtual Barrier Analytics, the FH-Series ID accurately detects and classifies human and vehicle threats moving at high or low speeds, minimizing false alarms and daily operations costs. Custom scheduling enables security operators to set intrusion analytics to run on visible streams during the day and thermal streams throughout the night, establishing optimized coverage for any lighting condition.
These analytics have been shown to reduce false alarms by 60% in a sample of 100 unique scenarios, including noise motion from shaking trees, animals that trigger as humans or vehicles, rapid changes in the cameras AGC, flat field correction events, as well as a shaking camera.
