Abstract

We present a new lightweight CNN-based algorithm for multi-frame optical flow estimation. Our solution introduces a double recurrence over spatial scale and time through repeated use of a generic “STaR” (SpatioTemporal Recurrent) cell. It includes (i) a temporal recurrence based on conveying learned features rather than optical flow estimates; (ii) an occlusion detection process which is coupled with optical flow estimation and therefore uses a very limited number of extra parameters. The resulting STaRFlow algorithm gives state-of-the-art performances on MPI Sintel and Kitti2015 and involves significantly less parameters than all other methods with comparable results.

Citation

@article{godet2020starflow,
  title={STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation},
  author={Godet, Pierre and Boulch, Alexandre and Plyer, Aur{\'e}lien and Besnerais, Guy Le},
  journal={arXiv preprint arXiv:2007.05481},
  year={2020}
}