Autonomous camera tracking powered by edge AI — replacing manual operation with intelligent object detection, predictive motion algorithms, and cinematic-quality PTZ control for live production.
A typical multi-camera live broadcast studio operates 4-8 cameras, each requiring a dedicated operator. At $40-60K per operator annually (salary, benefits, scheduling), a single studio faces $250K-500K in camera operation costs alone. Networks operating 10-20 studios spend $2M-10M per year on camera personnel — a cost that scales linearly with production volume.
The labor shortage compounds the problem. Experienced camera operators are aging out, and younger talent gravitates toward software and digital production roles. During live events — sports, news, concerts — the demand spike for skilled operators exceeds supply, forcing networks to accept lower production quality or pay premium freelance rates.
Existing robotic camera systems (Vinten, Shotoku, Ross) provide motorized PTZ control but require a human operator at the control panel. They automate the physical movement but not the creative decision-making: framing, subject tracking, shot selection, and smooth transitions. True autonomous camera operation requires computer vision, predictive motion algorithms, and broadcast-grade reliability — a combination that demands both FPGA video processing expertise and AI/ML engineering.
Promwad delivers an end-to-end autonomous camera tracking system that combines FPGA-based real-time video processing with AI object detection and predictive motion planning. The system mounts on existing PTZ camera heads, converting manual or remote-operated cameras into AI-autonomous units.
The architecture separates real-time control (FPGA) from AI inference (GPU/NPU), ensuring that camera movements remain smooth and broadcast-grade even during complex multi-subject scenes. A director-level API allows human operators to provide high-level instructions ("follow the speaker," "wide shot of panel") while the AI handles framing, tracking, and transitions.
Not entirely. The system replaces per-camera operators with a single director who provides high-level creative instructions to multiple AI cameras simultaneously. For premium live events (sports finals, concerts), a hybrid model — AI tracking with human override — delivers the best results. The value proposition is reducing a 6-person camera crew to 1-2 people, not eliminating human creative judgment.
Any PTZ head supporting VISCA/IP or NDI control protocols — including Panasonic, Sony, Canon, and Ross robotics. The FPGA capture module accepts SDI (BNC) and HDMI inputs. The system is designed as a retrofit module, not a replacement for existing camera infrastructure.
The predictive motion model combines Kalman filtering for trajectory estimation with LSTM neural networks trained on sport-specific movement patterns. The FPGA pre-processing stage provides motion vectors at frame rate, enabling the tracking engine to anticipate movement rather than react to it. End-to-end latency under 50ms ensures broadcast-grade smoothness.
Face recognition is used only for subject identification within the production context (identifying speakers, panelists, performers). No biometric data leaves the local system. The architecture supports GDPR-compliant modes where face recognition is replaced by clothing/position-based tracking for privacy-sensitive deployments.