Designing a Python-Based UAP Detection System: A Multi-Component CV Pipeline with OpenCV, YOLOv8, FFmpeg, and FastAPI

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A team is building a Python-based UAP detection system that sources public webcam feeds, handles different video formats, uses motion and object detection to track targets, analyzes trajectories for anomalies, and saves timestamped, metadata-rich footage, with a production bot already streaming and analyzing data and a 12‑week plan for further development.

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Designing a Python-Based UAP Detection System: A Multi-Component CV Pipeline with OpenCV, YOLOv8, FFmpeg, and FastAPI

Designed as a multi-component system, it aims to discover public webcam feeds, ingest diverse formats, and apply a multi-stage CV pipeline (motion detection, object detection with YOLOv8, tracking, trajectory analysis, and anomaly scoring) in Python using OpenCV, FFmpeg, and FastAPI to produce timestamped recordings with structured metadata while filtering false positives from birds, artifacts, aircraft, or satellites, culminating in a production-ready UAP Detection Bot that can stream from multiple formats, analyze trajectories, and store configurable footage, with a detailed architecture and a 12-week implementation plan guiding future work.