A Reproducible Videogrammetric Methodology for Kinematic Reconstruction of Road Accidents from Uncalibrated Stationary Cameras with GUM-Based Uncertainty Evaluation

Uncalibrated surveillance recordings can support road-accident reconstruction, but uncertain geometry, undocumented frame timing, and occlusion constrain the interpretation. This study presents a reproducible workflow combining geometric correction, feature tracking, kinematic regression, and conditional uncertainty propagation. An anonymised car–motorcycle collision illustrates the distinction between numerical reproducibility and independent measurement validation. Under a nominal 15 fps time base, the motorcycle two-phase fit gives an initial speed of 111.2 km/h with an expanded conditional uncertainty of 3.0 km/h (k = 2). The early visible car segment has a mean regression slope of 47.9 km/h; its later motion is fitted separately because a uniform initial phase is not established. Synthetic experiments assess estimator errors, interval coverage, geometric sensitivity, and changes in uncertainty-source ranking under specified conditions. The stopping analysis remains a scenario conditional on reaction, braking, timing, and geometric assumptions, with extrapolation outside the motorcycle observation interval. Missing independent calibration checks and frame timestamps prevent an end-to-end accuracy claim. The contribution is a transparent workflow separating observations, assumed distributions, model sensitivity and unresolved systematic effects.