Object detection using OpenCV
summary
A hands‑on OpenCV tutorial that loads pre‑trained Haar Cascades in a Jupyter notebook to detect face, eyes and smile with visible bounding boxes, shows color/BGR quirks, and explains tuning scaleFactor/minNeighbors/minSize. It contrasts detectors (R‑CNN family, SSD, YOLO, RetinaNet, CenterNet, Mask R‑CNN) and demos template matching, feature matching and contour detection. The demos run on images and real‑time video, require parameter tuning to remove false boxes, and recommend hundreds (300–500+) labeled samples.
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chapters (12)
- 0:00Introduction
- 2:07Agenda and goals
- 6:50What is computer vision
- 10:44Object detection uses
- 17:20Detection algorithms
- 26:20OpenCV overview
- 32:21Detection steps
- 38:08OpenCV techniques
- 44:10Harcascade explained
- 48:48Live coding demo
- 59:42Summary
- 1:00:44Q&A
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