Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 175 tok/s
Gemini 2.5 Pro 54 tok/s Pro
GPT-5 Medium 38 tok/s Pro
GPT-5 High 37 tok/s Pro
GPT-4o 108 tok/s Pro
Kimi K2 180 tok/s Pro
GPT OSS 120B 447 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Homography Guided Temporal Fusion for Road Line and Marking Segmentation (2404.07626v1)

Published 11 Apr 2024 in cs.CV

Abstract: Reliable segmentation of road lines and markings is critical to autonomous driving. Our work is motivated by the observations that road lines and markings are (1) frequently occluded in the presence of moving vehicles, shadow, and glare and (2) highly structured with low intra-class shape variance and overall high appearance consistency. To solve these issues, we propose a Homography Guided Fusion (HomoFusion) module to exploit temporally-adjacent video frames for complementary cues facilitating the correct classification of the partially occluded road lines or markings. To reduce computational complexity, a novel surface normal estimator is proposed to establish spatial correspondences between the sampled frames, allowing the HomoFusion module to perform a pixel-to-pixel attention mechanism in updating the representation of the occluded road lines or markings. Experiments on ApolloScape, a large-scale lane mark segmentation dataset, and ApolloScape Night with artificial simulated night-time road conditions, demonstrate that our method outperforms other existing SOTA lane mark segmentation models with less than 9\% of their parameters and computational complexity. We show that exploiting available camera intrinsic data and ground plane assumption for cross-frame correspondence can lead to a light-weight network with significantly improved performances in speed and accuracy. We also prove the versatility of our HomoFusion approach by applying it to the problem of water puddle segmentation and achieving SOTA performance.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (65)
  1. Cross-domain car detection using unsupervised image-to-image translation: From day to night. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2019.
  2. Magsac: marginalizing sample consensus. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10197–10205, 2019.
  3. Jonathan T. Barron. A general and adaptive robust loss function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
  4. Speeded-up robust features (surf). Computer vision and image understanding, 110(3):346–359, 2008.
  5. Surf: Speeded up robust features. In European conference on computer vision, pages 404–417. Springer, 2006.
  6. Unsupervised labeled lane markers using maps. In Proceedings of the IEEE International Conference on Computer Vision, 2019.
  7. Gms: Grid-based motion statistics for fast, ultra-robust feature correspondence. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4181–4190, 2017.
  8. A novel lane detection system with efficient ground truth generation. IEEE Transactions on Intelligent Transportation Systems, 13(1):365–374, 2011.
  9. Persformer: 3d lane detection via perspective transformer and the openlane benchmark. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVIII, pages 550–567. Springer, 2022.
  10. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 801–818, 2018.
  11. Progressive lidar adaptation for road detection. IEEE/CAA Journal of Automatica Sinica, 6(3):693–702, 2019.
  12. Deep image homography estimation. arXiv preprint arXiv:1606.03798, 2016.
  13. Homography estimation from image pairs with hierarchical convolutional networks. In Proceedings of the IEEE international conference on computer vision workshops, pages 913–920, 2017.
  14. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381–395, 1981.
  15. Single image water hazard detection using fcn with reflection attention units. In Proceedings of the European Conference on Computer Vision (ECCV), pages 105–120, 2018.
  16. Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521540518, second edition, 2004.
  17. Robust regression using iteratively reweighted least-squares. Communications in Statistics-theory and Methods, 6(9):813–827, 1977.
  18. Inter-region affinity distillation for road marking segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12486–12495, 2020.
  19. The apolloscape dataset for autonomous driving. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 954–960, 2018.
  20. Ceymo: See more on roads-a novel benchmark dataset for road marking detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3104–3113, 2022.
  21. Eigenlanes: Data-driven lane descriptors for structurally diverse lanes. In CVPR, 2022.
  22. Key points estimation and point instance segmentation approach for lane detection. IEEE Transactions on Intelligent Transportation Systems, 23(7):8949–8958, 2022.
  23. Deep homography estimation for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7652–7661, 2020.
  24. Vpgnet: Vanishing point guided network for lane and road marking detection and recognition. pages 1947–1955, 2017.
  25. Kenneth Levenberg. A method for the solution of certain non-linear problems in least squares. Quarterly of applied mathematics, 2(2):164–168, 1944.
  26. Temporal 3d fully connected network for water-hazard detection. In 2019 Digital Image Computing: Techniques and Applications (DICTA), pages 1–5, 2019.
  27. Nighttime lane markings recognition based on canny detection and hough transform. In IEEE International Conference on Real-time Computing and Robotics, pages 411–415. IEEE, 2016.
  28. Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers. In European conference on computer vision, pages 1–18. Springer, 2022.
  29. Road lane detection with gabor filters. In International Conference on Information System and Artificial Intelligence, pages 436–440. IEEE, 2016.
  30. Learning structure-aware semantic segmentation with image-level supervision. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2021.
  31. Condlanenet: a top-to-down lane detection framework based on conditional convolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3773–3782, 2021.
  32. Petrv2: A unified framework for 3d perception from multi-camera images. arXiv preprint arXiv:2206.01256, 2022.
  33. David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91–110, 2004.
  34. Locality preserving matching. International Journal of Computer Vision, 127(5):512–531, 2019.
  35. Groundnet: Monocular ground plane normal estimation with geometric consistency. In Proceedings of the 27th ACM International Conference on Multimedia, pages 2170–2178, 2019.
  36. Donald W Marquardt. An algorithm for least-squares estimation of nonlinear parameters. Journal of the society for Industrial and Applied Mathematics, 11(2):431–441, 1963.
  37. Deep semantic lane segmentation for mapless driving. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 869–875, 2018.
  38. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 31(5):1147–1163, 2015.
  39. Towards end-to-end lane detection: an instance segmentation approach. In 2018 IEEE Intelligent Vehicles Symposium (IV), pages 286–291, 2018.
  40. Spatial as deep: Spatial cnn for traffic scene understanding. volume 32, 2018.
  41. Jonah Philion. Fastdraw: Addressing the long tail of lane detection by adapting a sequential prediction network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11582–11591, 2019.
  42. Ultra fast structure-aware deep lane detection. In European Conference on Computer Vision, pages 276–291. Springer, 2020.
  43. Orb: An efficient alternative to sift or surf. In 2011 International conference on computer vision, pages 2564–2571. Ieee, 2011.
  44. Back to the Feature: Learning robust camera localization from pixels to pose. In CVPR, 2021.
  45. Structure-from-motion revisited. In Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
  46. Lane datasets for lane detection. In 2019 International Conference on Communication and Signal Processing (ICCSP), pages 0792–0796. IEEE, 2019.
  47. Beblid: Boosted efficient binary local image descriptor. Pattern recognition letters, 133:366–372, 2020.
  48. Coarse-to-fine feature mining for video semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision and Patern Recognition (CVPR), 2022.
  49. Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2446–2454, 2020.
  50. Hsi color model based lane-marking detection. In IEEE Intelligent Transportation Systems Conference, pages 1168–1172. IEEE, 2006.
  51. Keep your eyes on the lane: Real-time attention-guided lane detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 294–302, 2021.
  52. Polylanenet: Lane estimation via deep polynomial regression. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 6150–6156. IEEE, 2021.
  53. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR, 2019.
  54. Lane detection using directional random walks. In IEEE Intelligent Vehicles Symposium, pages 303–306. IEEE, 2008.
  55. Single-view view synthesis with multiplane images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 551–560, 2020.
  56. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  57. An approach of lane detection based on inverse perspective mapping. In International IEEE Conference on Intelligent Transportation Systems, pages 35–38. IEEE, 2014.
  58. View consistent purification for accurate cross-view localization. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 8197–8206, October 2023.
  59. Satellite image based cross-view localization for autonomous vehicle. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 3592–3599. IEEE, 2023.
  60. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077–12090, 2021.
  61. Fusionlane: Multi-sensor fusion for lane marking semantic segmentation using deep neural networks. IEEE Transactions on Intelligent Transportation Systems, 2020.
  62. End-to-end lane marker detection via row-wise classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 1006–1007, 2020.
  63. Large batch optimization for deep learning: Training bert in 76 minutes. arXiv preprint arXiv:1904.00962, 2019.
  64. Vil-100: A new dataset and a baseline model for video instance lane detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15681–15690, 2021.
  65. Stereo magnification: Learning view synthesis using multiplane images. arXiv preprint arXiv:1805.09817, 2018.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 1 tweet and received 0 likes.

Upgrade to Pro to view all of the tweets about this paper:

Start a free 7-day Pro trial