#Lucas-Kanade

Computer Vision Primer September 7, 2026

Feature Tracking Primer — How to Avoid Losing the Same Point Between Frames

Feature tracking is the process of matching the same physical point across frames in a video sequence. This article organizes template tracking, Lucas–Kanade optical flow, descriptor matching, RANSAC, outlier rejection, and Kalman prediction with equations and implementation steps, and explains the conditions under which Visual Odometry and SLAM break down, along with mitigations.

Fundamentals · 6 min Computer Vision Primer September 7, 2026

Optical Flow Primer — Reading Velocity and Structure from Image Motion

Optical Flow is a vector field describing which direction each pixel in a sequence of images has moved. This article works through the brightness-constancy constraint, Lucas–Kanade and Horn–Schunck, pyramids, dense flow versus sparse feature tracking, learning-based methods such as RAFT, and camera motion, dynamic objects, and evaluation methodology, with the underlying math.

Fundamentals · 5 min