Optical Flow Based Motion Detection for Autonomous Driving
This work addresses motion detection in autonomous driving, particularly for distant vehicles, but it appears incremental as it applies existing methods to a specific scenario.
The paper tackles motion detection for autonomous driving by training a neural network to classify motion status using optical flow field information, achieving high accuracy for distant vehicles and acceptable performance for nearby ones.
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 .