提出视频级道路障碍物分割新方法，提升连续帧一致性。
Road Obstacle Video Segmentation
- 利用视频时序相关性，改进连续帧分割一致性
- 在4个新基准上验证，长视频序列性能领先
- 适合自动驾驶感知系统研发人员参考
随着自动驾驶代理的广泛应用，道路障碍物的检测与分割对保障安全自主导航至关重要。然而，现有道路障碍物分割方法仅针对单帧处理，忽视了问题的时序特性，导致相邻帧预测结果不一致。本文证明道路障碍物分割本质上具有时序性，连续帧的分割图强相关。为此，我们构建并适配了四个道路障碍物视频分割评估基准，评估了11种先进图像与视频分割方法。此外，我们提出了基于视觉基础模型的两个强基线方法。所提方法在长视频序列上实现了新的最先进性能，为未来研究提供了重要启示。
原文摘要 · Abstract (English)
With the growing deployment of autonomous driving agents, the detection and segmentation of road obstacles have become critical to ensure safe autonomous navigation. However, existing road-obstacle segmentation methods are applied on individual frames, overlooking the temporal nature of the problem, leading to inconsistent prediction maps between consecutive frames. In this work, we demonstrate that the road-obstacle segmentation task is inherently temporal, since the segmentation maps for consecutive frames are strongly correlated. To address this, we curate and adapt four evaluation benchmarks for road-obstacle video segmentation and evaluate 11 state-of-the-art image- and video-based segmentation methods on these benchmarks. Moreover, we introduce two strong baseline methods based on vision foundation models. Our approach establishes a new state-of-the-art in road-obstacle video segmentation for long-range video sequences, providing valuable insights and direction for future research.
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