Velocity Estimation with Optical Flow
Connects physical-world motion, perception models, calibration assumptions, and embedded capture constraints.
Repo test video
System Overview
Vehicle speed estimation system using classical optical flow, RAFT, YOLO, Raspberry Pi, and camera-stream constraints.
Research/build system.
Implementation Signals
System flow
A compact view of how inputs move through processing, orchestration, validation, and output.
Video Stream
Vehicle ROI
Optical Flow
Motion Conversion
Velocity Output
Engineering decisions
Decision Record
Compare classical and deep optical flow
Problem: Real-time motion estimation has tradeoffs between speed, accuracy, and hardware constraints.
Approach: Explore Lucas-Kanade, Farneback, RAFT, YOLO ROI handling, and Pi Camera streaming.
Tradeoff: Deep methods add compute cost.
Outcome: Clearer understanding of calibration, frame rate, ROI stability, and embedded limits.
Results / Learnings
Repo
Package notes document RAFT, YOLO, KITTI evaluation, and Pi Camera streaming.
Learning
Speed estimation quality depends on calibration, distance, frame rate, and ROI stability.
Progressive depth
This page keeps the outcome and architecture visible first. Implementation stack, decisions, constraints, and media are available below so technical depth is opt-in rather than forced.
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Open