Fetching the paper…
Reading the bibliography…
Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems.
S. Lee, J.-S. Kim, J. S. Yoon, S. Shin, O. Bailo, N. Kim, T.-H. Lee, H. S. Hong, S.-H. Han, and I.-S. Kweon, “VPGNet: Vanishing point guided network for lane and road marking detection and recognition,” in International Conference on Computer Vision , 2017, pp. 1965–1973
1973
Earlier work this paper cites.
D. J. Kang, J. W. Choi, and I. Kweon, “Finding and tracking road lanes using “ line-snakes ”,” in IEEE Intelligent Vehicles Symposium (IV) , 1996, pp. 189–194
1996
Earlier work this paper cites.
Y. Wang and D. Shen, “Lane detection using catmull-rom spline,” in IEEE Intelligent Vehicles Symposium , 1998, pp. 51–57
1998
Earlier work this paper cites.
Y. Wang, K. Teoh, and D. Shen, “Lane detection and tracking using b-snake,” Image and Vision computing , vol. 22, pp. 269–280, 2004
2004
Earlier work this paper cites.
Y. He, H. Wang, and B. Zhang, “Color-based road detection in urban traffic scenes,” IEEE Transactions on Intelligent Transportation Systems , vol. 5, pp. 309–318, 2004
2004
Earlier work this paper cites.
J. McCall and M. Trivedi, “Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation,” IEEE Transactions on Intelligent Transportation Systems , vol. 7, pp. 20–37, 2006
2006
Earlier work this paper cites.
T. Suttorp and T. Bucher, “Learning of kalman filter parameters for lane detection,” in IEEE Intelligent Vehicles Symposium , 2006, pp. 552–557
2006
Earlier work this paper cites.
C. Caraffi, S. Cattani, and P. Grisleri, “Off-road path and obstacle detection using decision networks and stereo vision,” IEEE Transactions on Intelligent Transportation Systems , vol. 8, pp. 607–618, 2007
2007
Earlier work this paper cites.
C. Wojek and B. Schiele, “A dynamic conditional random field model for joint labeling of object and scene classes,” in European Conference on Computer Vision (ECCV) , 2008
2008
Earlier work this paper cites.
M. Aly, “Real time detection of lane markers in urban streets,” in IEEE Intelligent Vehicles Symposium , 2008, pp. 7–12
2008
Earlier work this paper cites.
A. Wedel, H. Badino, C. Rabe, H. Loose, U. Franke, and D. Cremers, “B-spline modeling of road surfaces with an application to free-space estimation,” IEEE Transactions on Intelligent Transportation Systems , vol. 10, pp. 572–583, 2009
2009
Earlier work this paper cites.
R. Danescu and S. Nedevschi, “Probabilistic lane tracking in difficult road scenarios using stereovision,” IEEE Transactions on Intelligent Transportation Systems , vol. 10, pp. 272–282, 2009
2009
Earlier work this paper cites.
A. Borkar, M. H. Hayes, and M. T. Smith, “Robust lane detection and tracking with ransac and kalman filter,” in IEEE International Conference on Image Processing (ICIP) , 2009, pp. 3261–3264
2009
Earlier work this paper cites.
H. Loose, U. Franke, and C. Stiller, “Kalman particle filter for lane recognition on rural roads,” in IEEE Intelligent Vehicles Symposium , 2009, pp. 60–65
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in IEEE Conference on Computer Vision and Pattern Recognition , 2009, pp. 248–255
2009
Earlier work this paper cites.
L. Chen, Q. Li, Q. Mao, and Q. Zou, “Block-constraint line scanning method for lane detection,” in IEEE Intelligent Vehicles Symposium , 2010, pp. 89–94
2010
Earlier work this paper cites.
S. Zhou, Y. Jiang, J. Xi, J. Gong, G. Xiong, and H. Chen, “A novel lane detection based on geometrical model and gabor filter,” in IEEE Intelligent Vehicles Symposium , 2010, pp. 59–64
2010
Earlier work this paper cites.
H.-C. Choi and S.-Y. Oh, “Illumination invariant lane color recognition by using road color reference, neural networks,” in The 2010 International Joint Conference on Neural Networks (IJCNN) , 2010, pp. 1–5
2010
Earlier work this paper cites.
A. Borkar, M. Hayes, and M. Smith, “Polar randomized hough transform for lane detection using loose constraints of parallel lines,” in International Conference on Acoustics, Speech and Signal Processing , 2011
2011
Earlier work this paper cites.
A. G. Linarth and E. Angelopoulou, “On feature templates for particle filter based lane detection,” in IEEE Conference on Intelligent Transportation Systems (ITSC) , 2011, pp. 1721–1726
2011
Earlier work this paper cites.
J. Hur, S.-N. Kang, and S.-W. Seo, “Multi-lane detection in urban driving environments using conditional random fields,” in IEEE Intelligent Vehicles Symposium (IV) , 2013, pp. 1297–1302
2013
Earlier work this paper cites.
S. Yenikaya, G. Yenikaya, and E. Düven, “Keeping the vehicle on the road: A survey on on-road lane detection systems,” ACM Computing Surveys , vol. 46, no. 2, pp. 1–43, 2013
2013
Earlier work this paper cites.
J. Deng and Y. Han, “A real-time system of lane detection and tracking based on optimized ransac b-spline fitting,” in Proceedings of the Research in Adaptive and Convergent Systems (RACS) , 2013
2013
Earlier work this paper cites.
A. Hillel, R. Lerner, D. Levi, and G. Raz, “Recent progress in road and lane detection: a survey,” Machine Vision and Applications , vol. 25, no. 3, pp. 727–745, 2014
2014
Cited alongside, same era.
Q. Li, L. Chen, M. Li, S.-L. Shaw, and A. Nüchter, “A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios,” IEEE Transactions on Vehicular Technology , vol. 63, pp. 540–555, 2014
2014
Cited alongside, same era.
A. Mammeri, A. Boukerche, and G. Lu, “Lane detection and tracking system based on the mser algorithm, hough transform and kalman filter,” in ACM international conference on Modeling, analysis and simulation of wireless and mobile systems , 2014
2014
Cited alongside, same era.
M. A. Selver, “Segmentation of abdominal organs from ct using a multi-level, hierarchical neural network strategy,” Computer methods and programs in biomedicine , vol. 113, no. 3, pp. 830–852, 2014
2014
Cited alongside, same era.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 39, pp. 2481–2495, 2017
2017
Later among the works it cites.
J. Kim and C. Park, “End-to-end ego lane estimation based on sequential transfer learning for self-driving cars,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2017, pp. 1194–1202
2017
Later among the works it cites.
O. Bailo, S. Lee, F. Rameau, J. S. Yoon, and I.-S. Kweon, “Robust road marking detection and recognition using density-based grouping and machine learning techniques,” in IEEE Winter Conference on Applications of Computer Vision (WACV) , 2017, pp. 760–768
2017
Later among the works it cites.
P. Tokmakov, K. Alahari, and C. Schmid, “Learning video object segmentation with visual memory,” 2017 IEEE International Conference on Computer Vision (ICCV) , pp. 4491–4500, 2017
2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio, “Generative adversarial nets,” in NIPS , 2014, pp. 2672–2680
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2014
Cited alongside, same era.
R. B. Girshick, “Fast r-cnn,” in IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 1440–1448
2015
Cited alongside, same era.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representations , 2015
2015
Cited alongside, same era.
E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 39, pp. 640–651, 2015
2015
Cited alongside, same era.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer Assisted Intervention , 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Later among the works it cites.
H. Zhu, R. Vial, and S. Lu, “Tornado: A spatio-temporal convolutional regression network for video action proposal,” 2017 IEEE International Conference on Computer Vision (ICCV) , pp. 5814–5822, 2017
2017
Later among the works it cites.
R. Villegas, J. Yang, S. Hong, X. Lin, and H. Lee, “Decomposing motion and content for natural video sequence prediction,” in International Conference on Learning Representations (ICLR) , 2017
2017
Later among the works it cites.
J. Ke, H. Zheng, H. Yang, and X. Chen, “Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach,” Transportation Research Part C: Emerging Technologies , vol. 85, pp. 591–608, 2017
2017
Later among the works it cites.
W. Wang, D. Zhao, W. Han, and J. Xi, “A learning-based approach for lane departure warning systems with a personalized driver model,” IEEE Transactions on Vehicular Technology , 2018
2018
Later among the works it cites.
Y. Huang, S. tao Chen, Y. Chen, Z. Jian, and N. Zheng, “Spatial-temproal based lane detection using deep learning,” in IFIP International Conference on Artificial Intelligence Applications and Innovations , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
T. Bruls, W. Maddern, A. A. Morye, and P. Newman, “Mark yourself: Road marking segmentation via weakly-supervised annotations from multimodal data,” in IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 1863–1870
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
N. Xu, L. Yang, Y. Fan, J. Yang, D. Yue, Y. Liang, B. L. Price, S. Cohen, and T. S. Huang, “Youtube-vos: Sequence-to-sequence video object segmentation,” in European Conference on Computer Vision (ECCV) , 2018
2018
Later among the works it cites.
H. Song, W. Wang, S. Zhao, J. Shen, and K.-M. Lam, “Pyramid dilated deeper convlstm for video salient object detection,” in European Conference on Computer Vision (ECCV) , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
Q. Zou, L. Ni, Q. Wang, Q. Li, and S. Wang, “Robust gait recognition by integrating inertial and rgbd sensors,” IEEE Transactions on Cybernetics , vol. 48, no. 4, pp. 1136–1150, 2018
2018
Later among the works it cites.
L. Li, K. Ota, and M. Dong, “Humanlike driving: Empirical decision-making system for autonomous vehicles,” IEEE Transactions on Vehicular Technology , vol. 67, no. 8, pp. 6814–6823, 2018
2018
Later among the works it cites.
L. Chen, W. Zhan, W. Tian, Y. He, and Q. Zou, “Deep integration: A multi-label architecture for road scene recognition,” IEEE Transactions on Image Processing , vol. 28, no. 10, pp. 4883–4898, 2019
2019
Closest in time.
Z. Zhang, Q. Zou, Y. Lin, L. Chen, and S. Wang, “Improved deep hashing with soft pairwise similarity for multi-label image retrieval,” IEEE Transactions on Multimedia , pp. 1–14, 2019
2019
Closest in time.
Q. Zou, Z. Zhang, Q. Li, X. Qi, Q. Wang, and S. Wang, “Deepcrack: Learning hierarchical convolutional features for crack detection,” IEEE Transactions on Image Processing , vol. 28, no. 3, pp. 1498–1512, 2019
2019
Closest in time.
X. Liang, G. Wang, M. R. Min, Y. Qi, and Z. Han, “A deep spatio-temporal fuzzy neural network for passenger demand prediction,” in SIAM International Conference on Data Mining , 2019, pp. 100–108
2019
Closest in time.