Understanding Road Layout from Videos as a Whole

TL;DR

This paper presents a novel approach for inferring complex road layouts from videos by integrating camera motion, context cues, and long-term information, significantly improving accuracy and consistency over previous methods.

Contribution

It introduces a new model combining LSTM and Feature Transform Module that leverages camera motion and context cues for consistent, accurate road layout prediction from videos.

Findings

Incorporating global and contextual cues enhances prediction accuracy.

LSTM and FTM modules improve prediction consistency.

Proposed method outperforms state-of-the-art techniques significantly.

Abstract

In this paper, we address the problem of inferring the layout of complex road scenes from video sequences. To this end, we formulate it as a top-view road attributes prediction problem and our goal is to predict these attributes for each frame both accurately and consistently. In contrast to prior work, we exploit the following three novel aspects: leveraging camera motions in videos, including context cuesand incorporating long-term video information. Specifically, we introduce a model that aims to enforce prediction consistency in videos. Our model consists of one LSTM and one Feature Transform Module (FTM). The former implicitly incorporates the consistency constraint with its hidden states, and the latter explicitly takes the camera motion into consideration when aggregating information along videos. Moreover, we propose to incorporate context information by introducing road participants, e.g. objects, into our model. When the entire video sequence is available, our model is also able to encode both local and global cues, e.g. information from both past and future frames. Experiments on two data sets show that: (1) Incorporating either globalor contextual cues improves the prediction accuracy and leveraging both gives the best performance. (2) Introducing the LSTM and FTM modules improves the prediction consistency in videos. (3) The proposed method outperforms the SOTA by a large margin.