Classical CV Multi-Camera Stitching and Sports Motion Tracking
Pain Points (Public)
Budget sports filming rigs need automated broadcast-style tracking, but standard deep-learning pipelines require costly GPU hardware, high power consumption, and specialized training datasets that small sports organizations cannot accommodate.
Suggested Approach (Public)
Build a real-time panoramic stitching pipeline for dual fixed cameras using feature-based homography, coupled with traditional computer vision algorithms like background subtraction and optical flow to follow game action without neural network inference.
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Opportunity assessment PRO
Development brief PRO
- Position as a lightweight, CPU-only CLI pipeline using OpenCV and FFmpeg to ingest synchronized dual-camera video files, targeting budget sports setups wanting Veo-like pan-and-scan outputs without GPU servers.
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- Build an OpenCV and C++ dual-video ingest module that aligns and synchronizes two fixed-camera video streams using static homography stitching.
Competitor evidence PRO
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