The Realities of Real-Time Computer Vision at Scale
Facial detection and recognition operate through deep convolutional neural networks. While our proprietary models are aggressively optimized for CPU execution via OpenVINO and ONNX Runtime, multi-camera processing demands adequate processor throughput and memory bandwidth.
During campus field deployments, we observed that entry-level dual-core systems (such as older Intel Core i3 4th/6th Gen processors) struggle when ingesting two concurrent RTSP high-resolution streams, occasionally resulting in frame drops or stutter. For seamless walk-through recognition, choosing the appropriate hardware tier ensures decades of trouble-free operation.
Recommended System Specifications
Review our tiered hardware recommendation matrix to equip your school entrance with the optimal workstation:
Key Hardware Insights
- CPU Clock Speed & Physical Cores: Aim for processors with at least 4 physical cores and 8 threads. Intel Core i5 8th Gen or AMD Ryzen 5 3600 or higher provides ample headroom for decoding two concurrent 1080p RTSP camera streams.
- RAM Dual-Channel Configuration: 8 GB is the absolute minimum requirement. 16 GB DDR4 running in dual-channel mode increases memory bandwidth by over 30%, which significantly accelerates neural network tensor operations.
- Network Interface Card (Gigabit NIC): Connect the terminal PC to your network switch via a dedicated Cat6 Ethernet cable rather than Wi-Fi. Wi-Fi packet drops and jitter can corrupt RTSP frames and induce decoding latency.
Getting Started
Have an existing campus PC and want to test compatibility? Download our TrackingFace Local Unit Terminal or contact our technical team at contact@trackingface.com for personalized hardware sizing.