mlsanityData to 14 Oct 2026

Road Obstacle Video Segmentation

arXiv 2509.13181 ↗First posted 16 Sep 2025cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 47 of its 53 references that are in the map. First posted on arXiv 16 Sep 2025.

Builds on

47 of 53 references.

References · 47

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  5. 2023Segment Anythingcited by 8,234
  6. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  7. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  8. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  9. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  10. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
  11. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  12. 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
35 more
  1. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  2. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,351
  3. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  4. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,152
  5. 2016RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentationcited by 465
  6. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  7. 2019Deep Ensembles: A Loss Landscape Perspectivecited by 443
  8. 2023Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistlescited by 290
  9. 2023Data Filtering Networkscited by 250
  10. 2018The ApolloScape Open Dataset for Autonomous Driving and its Applicationcited by 190
  11. 2021Mask2Former for Video Instance Segmentationcited by 171
  12. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  13. 2018Evaluating Bayesian Deep Learning Methods for Semantic Segmentationcited by 143
  14. 2020Video Panoptic Segmentationcited by 142
  15. 2019Detecting the Unexpected via Image Resynthesiscited by 137
  16. 2019The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentationcited by 122
  17. 2016Lost and Found: Detecting Small Road Hazards for Self-Driving Vehiclescited by 117
  18. 2021Pixel-wise Anomaly Detection in Complex Driving Scenescited by 109
  19. 2020Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentationcited by 96
  20. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 90
  21. 2021Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenescited by 83
  22. 2021Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentationcited by 77
  23. 2023DVIS: Decoupled Video Instance Segmentation Frameworkcited by 77
  24. 2022RbA: Segmenting Unknown Regions Rejected by Allcited by 67
  25. 2018Discriminative out-of-distribution detection for semantic segmentationcited by 47
  26. 2023Unmasking Anomalies in Road-Scene Segmentationcited by 45
  27. 2023On Advantages of Mask-level Recognition for Outlier-aware Segmentationcited by 42
  28. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  29. 2023Maskomaly:Zero-Shot Mask Anomaly Segmentationcited by 34
  30. 2020Dense open-set recognition with synthetic outliers generated by Real NVPcited by 29
  31. 2022Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objectscited by 19
  32. 2023Mask2Anomaly: Mask Transformer for Universal Open-set Segmentationcited by 18
  33. 2020LiDAR guided Small obstacle Segmentationcited by 17
  34. 2024VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detectioncited by 8
  35. 2023Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentationcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Safety-oriented sidewalk and road segmentation for smartphone-based assistive navigationcited by 0
  2. 2026Combining Foundation Model Confidence and Monocular Depth for Training-Free Out-of-Distribution Segmentationcited by 0