Leveraging Python for Real-Time Image processing
summary
Realtime pipeline that ingests camera streams over RTSP or local device, runs OpenCV (cv2) for frame capture and SSD/YOLO object detection, then crops, aligns and embeds faces with a Face Detection model for identification; tracking avoids re-identifying every frame. SSD runs ~22 fps, Retina-like face models take ~2s/frame while object models hit ~35–50 fps on an old laptop. Detections and identities go to a timeseries DB and trigger Telegram alerts; a home prototype runs and successfully detects/alerts on suspicious visitors.
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chapters (8)
- 0:00Introduction and motivation
- 2:44Problem and solution overview
- 4:11Capturing images with Python
- 7:50Object detection algorithms
- 11:32Face recognition pipeline
- 16:30Database and notifications
- 17:54Alert logic and demo
- 19:27Key takeaways and conclusion
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