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Monocular Velocity: Camera-based vehicle velocity estimation from monocular video

July 2020

tl;dr: Relative velocity estimation from a sequence of monocular images, taken with a moving camera.

Overall impression

This is the winning entry to the monocular velocity estimation challenge. Lightweight trajectory based features (list of bbox location) are good enough. Better than full solution with depth and optical flow features.

The SOTA error is around 1.12 m/s, as compared to the GT error of 0.71 m/s.

Key ideas

  • TuSimple dataset:
    • 20 fps, 40 frames long
    • distance ranging from 5 to 90 meters
    • bbox annotated on the last frame
  • Input: two stacked images
  • Off the shelf tools:
    • Tracking with openCV lib (Median Flow + MIL).
    • Depth with Monodepth
    • Optical flow with FlowNet2.
  • Feature extraction:
    • Spatial: shrink the bbox by 10% then calculate the mean
    • Temporal: Gaussian smoothed with width=5.
  • Location and velocity prediction:
    • MLP: 4-layer
    • Split into 3 distance bins based on bbox size (20m, 45m as separator)
  • 3 Models in different distance bins is better than one combined model

Technical details

  • Both depth and optical flow degrades beyond near range (> 20m). It can achieve the best performance in near range (< 20m)
  • Joint training with location leads to slightly better performance due to small dataset size
  • Ensemble of 5 models for each of the 3 distance bins (from 5-fold cross validation)

Notes