Novel Video Prediction for Large-scale Scene using Optical Flow
This addresses video prediction for autonomous driving in complex scenes, but it appears incremental as it builds on existing optical flow approaches.
The paper tackles video prediction in complex urban scenes for autonomous driving by proposing an optical flow conditioned method that uses only video and optical flow sequences, achieving effectiveness demonstrated on KITTI and Cityscapes datasets.
Making predictions of future frames is a critical challenge in autonomous driving research. Most of the existing methods for video prediction attempt to generate future frames in simple and fixed scenes. In this paper, we propose a novel and effective optical flow conditioned method for the task of video prediction with an application to complex urban scenes. In contrast with previous work, the prediction model only requires video sequences and optical flow sequences for training and testing. Our method uses the rich spatial-temporal features in video sequences. The method takes advantage of the motion information extracting from optical flow maps between neighbor images as well as previous images. Empirical evaluations on the KITTI dataset and the Cityscapes dataset demonstrate the effectiveness of our method.