Heatmap-based Vanishing Point boosts Lane Detection

S. Lee, J. Kim, J. Shin Yoon, S. Shin, O. Bailo, N. Kim, T.-H. Lee, H. Seok Hong, S.-H. Han, and I. So Kweon, “Vpgnet: Vanishing point guided network for lane and road marking detection and recognition,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 1947–1955

1955

Earlier work this paper cites.

H.-Y. Cheng, B.-S. Jeng, P.-T. Tseng, and K.-C. Fan, “Lane detection with moving vehicles in the traffic scenes,” IEEE Transactions on intelligent transportation systems , vol. 7, no. 4, pp. 571–582, 2006

2006

Earlier work this paper cites.

P. Moghadam, J. A. Starzyk, and W. S. Wijesoma, “Fast vanishing-point detection in unstructured environments,” IEEE Transactions on Image Processing , vol. 21, no. 1, pp. 425–430, 2011

Original

2007

Earlier work this paper cites.

M. Aly, “Real time detection of lane markers in urban streets,” in 2008 IEEE Intelligent Vehicles Symposium . IEEE, 2008, pp. 7–12

2008

Earlier work this paper cites.

H. Jung, J. Min, and J. Kim, “An efficient lane detection algorithm for lane departure detection,” in 2013 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2013, pp. 976–981

2013

Earlier work this paper cites.

J. Shi, J. Wang, and F. Fu, “Fast and robust vanishing point detection for unstructured road following,” IEEE Transactions on Intelligent Transportation Systems , vol. 17, no. 4, pp. 970–979, 2015

2015

Earlier work this paper cites.

A. Newell, K. Yang, and J. Deng, “Stacked hourglass networks for human pose estimation,” in European conference on computer vision . Springer, 2016, pp. 483–499

2016

Earlier work this paper cites.

J. Li, X. Mei, D. V. Prokhorov, and D. Tao, “Deep neural network for structural prediction and lane detection in traffic scene,” IEEE Transactions on Neural Networks , vol. 28, no. 3, pp. 690–703, 2017

2017

Cited alongside, same era.

J. H. Yoo, S. Lee, S. Park, and D. H. Kim, “A robust lane detection method based on vanishing point estimation using the relevance of line segments,” IEEE Transactions on Intelligent Transportation Systems , vol. 18, no. 12, pp. 3254–3266, 2017

2017

Cited alongside, same era.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 4, pp. 834–848, 2017

2017

Cited alongside, same era.

D. Neven, B. De Brabandere, S. Georgoulis, M. Proesmans, and L. Van Gool, “Towards end-to-end lane detection: an instance segmentation approach,” in 2018 IEEE intelligent vehicles symposium (IV) . IEEE, 2018, pp. 286–291

W. Zhou, S. Lv, Q. Jiang, and L. Yu, “Deep road scene understanding,” IEEE Signal Processing Letters , vol. 26, no. 4, pp. 587–591, 2019

2019

Later among the works it cites.

Y. Hou, Z. Ma, C. Liu, and C. C. Loy, “Learning lightweight lane detection cnns by self attention distillation,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 1013–1021

2019

Later among the works it cites.

J. Philion, “Fastdraw: Addressing the long tail of lane detection by adapting a sequential prediction network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 11 582–11 591

2019

Later among the works it cites.

H. Lee, K. Sohn, and D. Min, “Unsupervised low-light image enhancement using bright channel prior,” IEEE Signal Processing Letters , vol. 27, pp. 251–255, 2020

2020

Closest in time.