Optical-Flow-Estimation

A comparative study of optical flow estimation methods: Lucas-Kanade (sparse) vs RAFT (dense deep learning). Lucas-Kanade tracks 88 feature points with ~1.52px flow. RAFT computes 230K per-pixel vectors capturing complex motions, occlusions, and subtle movements.

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$33.4K

Market Value

$3.3K

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Growth over time

1 data points · 2026-07-18 → 2026-07-18

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What is the Tayyabah-Rehman/Optical-Flow-Estimation GitHub project? Description: "A comparative study of optical flow estimation methods: Lucas-Kanade (sparse) vs RAFT (dense deep learning). Lucas-Kanade tracks 88 feature points with ~1.52px flow. RAFT computes 230K per-pixel vectors capturing complex motions, occlusions, and subtle movements.". Written in Jupyter Notebook. Explain what it does, its main use cases, key features, and who would benefit from using it.

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