Wprowadzenie niestandardowego oprogramowania do rozpoznawania twarzy

We began with intensive workshops to understand the project goals and the challenges at hand — like handling poor video quality, enabling real-time processing, and ensuring the system could scale. Our team performed a detailed audit of the client’s CCTV setup, assessing camera types, frame rates, and video quality to make sure the custom facial recognition software would work reliably in real-world conditions.

Projektowanie systemu i planowanie architektury

Next, our team designed a scalable, distributed architecture capable of processing multiple live streams simultaneously. Each part of the system — face detection, preprocessing, and recognition — was built as an independent component, ensuring smooth data flow and fault tolerance. We also mapped out integration points to connect the solution with the client’s existing CCTV network.

Rozwój i wdrażanie

We followed an Agile development approach, delivering results in stages and gathering regular feedback to refine the system. Here’s how we tackled each critical area:

• Wykrywanie i wyrównywanie twarzy: Our engineers built and optimized the detection pipeline to stabilize incoming video, addressing challenges like inconsistent lighting, unusual angles, and movement.
• Wstępne przetwarzanie obrazu: Our team enhanced video frames with filtering and stabilization, ensuring only clean, usable frames moved forward for recognition.
• Modele rozpoznawania: Using real-world video samples, we fine-tuned the recognition pipeline to accurately identify faces, even under extreme angles or partial obstructions.
• Optymalizacja przetwarzania wsadowego: To manage large video volumes efficiently, our optimized batch processing workflows for archived footage while reducing the load on real-time operations.

At every sprint, we conducted rigorous testing and performance monitoring to address bottlenecks and support consistent progress.