Calibration-Free Vehicle Speed Estimation: A Monocular Keypoint-Template Approach
This paper proposes a calibration-free framework for reliably and effectively estimating vehicle speeds from monocular videos, without relying on roadway features, camera calibration, or roadway-feature-based reference objects. The proposed framework estimates vehicle speeds using a 36-keypoint vehicle template and a homography matrix updated at each frame. A YOLO-based keypoint detection module is trained on diverse datasets, and two estimation strategies are compared: keypoint-only tracking and warped optical flow with dense spatial aggregation. Speed is estimated by projecting displacements
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 50%Showcase: geolocating a dashcam video without GPS, only from the footage [P] →
- FuzzySimilar title/name (fuzzy) · 59%deepspeedai/DeepSpeed →
“Fuzzy title match (0.73): “Calibration-Free Vehicle Speed Estimation: A Monocular Keypo” ≈ “deepspeedai/DeepSpeed””
- LinkedLinked via arxiv author · 85%Gaofeng Su →
“Calibration-Free Vehicle Speed Estimation: A Monocular Keypoint-Template Approach”
- LinkedLinked via arxiv author · 85%Keya Li →
“Calibration-Free Vehicle Speed Estimation: A Monocular Keypoint-Template Approach”
- LinkedLinked via arxiv author · 85%Raja Sengupta →
“Calibration-Free Vehicle Speed Estimation: A Monocular Keypoint-Template Approach”
- LinkedLinked via arxiv author · 85%Kara M. Kockelman →
“Calibration-Free Vehicle Speed Estimation: A Monocular Keypoint-Template Approach”