# Optical Flow Based Motion Detection for Autonomous Driving

arXiv: https://arxiv.org/abs/2203.11693 · HTML: https://mlsanity.com/paper/2203.11693/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation](https://mlsanity.com/paper/2103.04524/) (2021)
- [An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes](https://mlsanity.com/paper/1811.08290/) (2018)
- [A Feedback Neural Network for Small Target Motion Detection in Cluttered Backgrounds](https://mlsanity.com/paper/1805.00342/) (2018)

## Built on by (most cited first, 1 of 1)

- [Erasing the Ephemeral: Joint Camera Refinement and Transient Object Removal for Street View Synthesis](https://mlsanity.com/paper/2311.17634/) (2023)

## Abstract (arXiv)

Motion detection is a fundamental but challenging task for autonomous driving. In particular scenes like highway, remote objects have to be paid extra attention for better controlling decision. Aiming at distant vehicles, we train a neural network model to classify the motion status using optical flow field information as the input. The experiments result in high accuracy, showing that our idea is viable and promising. The trained model also achieves an acceptable performance for nearby vehicles. Our work is implemented in PyTorch. Open tools including nuScenes, FastFlowNet and RAFT are used. Visualization videos are available at https://www.youtube.com/playlist?list=PLVVrWgq4OrlBnRebmkGZO1iDHEksMHKGk .