Built on

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” vol. 9, pp. 1735–80, 12 1997
1997
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097–1105. [Online]. Available: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
2012
Earlier work this paper cites.
J. Fritsch, T. Kuhnl, and A. Geiger, “A new performance measure and evaluation benchmark for road detection algorithms,” in Intelligent Transportation Systems-(ITSC), 2013 16th International IEEE Conference on . IEEE, 2013, pp. 1693–1700
2013
Earlier work this paper cites.
H. Yoo, U. Yang, and K. Sohn, “Gradient-enhancing conversion for illumination-robust lane detection,” IEEE Transactions on Intelligent Transportation Systems , vol. 14, no. 3, pp. 1083–1094, 2013
2013
Earlier work this paper cites.
N. Kalchbrenner and P. Blunsom, “Recurrent Continuous Translation Models,” Emnlp , no. October, pp. 1700–1709, 2013
2013
Earlier work this paper cites.
A. B. 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
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” CoRR , vol. abs/1409.4842, 2014. [Online]. Available: http://arxiv.org/abs/1409.4842
Original
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR , vol. abs/1409.1556, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
Original
2014
Earlier work this paper cites.
J. Chung, Ç. Gülçehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” CoRR , vol. abs/1412.3555, 2014. [Online]. Available: http://arxiv.org/abs/1412.3555
Original
2014
Earlier work this paper cites.
J. Son, H. Yoo, S. Kim, and K. Sohn, “Real-time illumination invariant lane detection for lane departure warning system,” Expert Systems with Applications , vol. 42, no. 4, pp. 1816–1824, 2015
2015
Earlier work this paper cites.
S. Venugopalan, M. Rohrbach, J. Donahue, R. Mooney, T. Darrell, and K. Saenko, “Sequence to sequence - Video to text,” in Proceedings of the IEEE International Conference on Computer Vision , vol. 2015 International Conference on Computer Vision, ICCV 2015, 2015, pp. 4534–4542
2015
Earlier work this paper cites.

Similar

X. SHI, Z. Chen, H. Wang, D.-Y. Yeung, W.-k. Wong, and W.-c. WOO, “Convolutional lstm network: A machine learning approach for precipitation nowcasting,” in Advances in Neural Information Processing Systems 28 , C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Eds. Curran Associates, Inc., 2015, pp. 802–810. [Online]. Available: http://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting.pdf
2015
Cited alongside, same era.
C.-A. Brust, S. Sickert, M. Simon, E. Rodner, and J. Denzler, “Efficient convolutional patch networks for scene understanding,” in CVPR Scene Understanding Workshop , 2015
2015
Cited alongside, same era.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3431–3440
2015
Cited alongside, same era.
D. Levi, N. Garnett, E. Fetaya, and I. Herzlyia, “Stixelnet: A deep convolutional network for obstacle detection and road segmentation.” in BMVC , 2015, pp. 109–1

Then

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” CoRR , vol. abs/1610.02357, 2016. [Online]. Available: http://arxiv.org/abs/1610.02357
Original
2016
Later among the works it cites.
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu, “Pixel recurrent neural networks,” CoRR , vol. abs/1601.06759, 2016. [Online]. Available: http://arxiv.org/abs/1601.06759
Original
2016
Later among the works it cites.
J. Redmon and A. Farhadi, “Yolo9000: better, faster, stronger,” arXiv preprint arXiv:1612.08242 , 2016
Original
2016
Later among the works it cites.
M. Teichmann, M. Weber, M. Zoellner, R. Cipolla, and R. Urtasun, “Multinet: Real-time joint semantic reasoning for autonomous driving,” arXiv preprint arXiv:1612.07695 , 2016
Original
2016
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2018

Road Segmentation Using CNN with GRU

Lyu, Yecheng, Huang, Xinming

Understand

This paper presents an accurate and fast algorithm for road segmentation using convolutional neural network (CNN) and gated recurrent units (GRU).

For autonomous vehicles, road segmentation is a fundamental task that can provide the drivable area for path planning.
The existing deep neural network based segmentation algorithms usually take a very deep encoder-decoder structure to fuse pixels, which requires heavy computations, large memory and long processing time.
Hereby, a CNN-GRU network model is proposed and trained to perform road segmentation using data captured by the front camera of a vehicle.

Built on

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” vol. 9, pp. 1735–80, 12 1997
1997
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097–1105. [Online]. Available: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
2012
Earlier work this paper cites.
J. Fritsch, T. Kuhnl, and A. Geiger, “A new performance measure and evaluation benchmark for road detection algorithms,” in Intelligent Transportation Systems-(ITSC), 2013 16th International IEEE Conference on . IEEE, 2013, pp. 1693–1700
2013
Earlier work this paper cites.
H. Yoo, U. Yang, and K. Sohn, “Gradient-enhancing conversion for illumination-robust lane detection,” IEEE Transactions on Intelligent Transportation Systems , vol. 14, no. 3, pp. 1083–1094, 2013
2013
Earlier work this paper cites.
N. Kalchbrenner and P. Blunsom, “Recurrent Continuous Translation Models,” Emnlp , no. October, pp. 1700–1709, 2013
2013
Earlier work this paper cites.
A. B. 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
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” CoRR , vol. abs/1409.4842, 2014. [Online]. Available: http://arxiv.org/abs/1409.4842
Original
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR , vol. abs/1409.1556, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
Original
2014
Earlier work this paper cites.
J. Chung, Ç. Gülçehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” CoRR , vol. abs/1412.3555, 2014. [Online]. Available: http://arxiv.org/abs/1412.3555
Original
2014
Earlier work this paper cites.
J. Son, H. Yoo, S. Kim, and K. Sohn, “Real-time illumination invariant lane detection for lane departure warning system,” Expert Systems with Applications , vol. 42, no. 4, pp. 1816–1824, 2015
2015
Earlier work this paper cites.
S. Venugopalan, M. Rohrbach, J. Donahue, R. Mooney, T. Darrell, and K. Saenko, “Sequence to sequence - Video to text,” in Proceedings of the IEEE International Conference on Computer Vision , vol. 2015 International Conference on Computer Vision, ICCV 2015, 2015, pp. 4534–4542
2015
Earlier work this paper cites.

Similar

X. SHI, Z. Chen, H. Wang, D.-Y. Yeung, W.-k. Wong, and W.-c. WOO, “Convolutional lstm network: A machine learning approach for precipitation nowcasting,” in Advances in Neural Information Processing Systems 28 , C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Eds. Curran Associates, Inc., 2015, pp. 802–810. [Online]. Available: http://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting.pdf
2015
Cited alongside, same era.
C.-A. Brust, S. Sickert, M. Simon, E. Rodner, and J. Denzler, “Efficient convolutional patch networks for scene understanding,” in CVPR Scene Understanding Workshop , 2015
2015
Cited alongside, same era.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3431–3440
2015
Cited alongside, same era.
D. Levi, N. Garnett, E. Fetaya, and I. Herzlyia, “Stixelnet: A deep convolutional network for obstacle detection and road segmentation.” in BMVC , 2015, pp. 109–1

Then

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” CoRR , vol. abs/1610.02357, 2016. [Online]. Available: http://arxiv.org/abs/1610.02357
Original
2016
Later among the works it cites.
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu, “Pixel recurrent neural networks,” CoRR , vol. abs/1601.06759, 2016. [Online]. Available: http://arxiv.org/abs/1601.06759
Original
2016
Later among the works it cites.
J. Redmon and A. Farhadi, “Yolo9000: better, faster, stronger,” arXiv preprint arXiv:1612.08242 , 2016
Original
2016
Later among the works it cites.
M. Teichmann, M. Weber, M. Zoellner, R. Cipolla, and R. Urtasun, “Multinet: Real-time joint semantic reasoning for autonomous driving,” arXiv preprint arXiv:1612.07695 , 2016
Original
2016
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2015

Cited alongside, same era.

D. P. Kingma and J. L. Ba, “Adam: a Method for Stochastic Optimization,” International Conference on Learning Representations 2015 , pp. 1–15, 2015

2015

Cited alongside, same era.

A. Khosroshahi, E. Ohn-Bar, and M. M. Trivedi, “Surround vehicles trajectory analysis with recurrent neural networks,” in Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on . IEEE, 2016, pp. 2267–2272

2016

Cited alongside, same era.

Y. Qi, S. Zhang, L. Qin, H. Yao, Q. Huang, J. Lim, and M. Yang, “Hedged deep tracking,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 , 2016, pp. 4303–4311. [Online]. Available: https://doi.org/10.1109/CVPR.2016.466

2016

Cited alongside, same era.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2818–2826

2016

Cited alongside, same era.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

2016

Cited alongside, same era.

A. Canziani, A. Paszke, and E. Culurciello, “An analysis of deep neural network models for practical applications,” CoRR , vol. abs/1605.07678, 2016. [Online]. Available: http://arxiv.org/abs/1605.07678

Original

2016

Cited alongside, same era.

F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer, “Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1mb model size,” CoRR , vol. abs/1602.07360, 2016. [Online]. Available: http://arxiv.org/abs/1602.07360

Original

2016

Cited alongside, same era.

A. Laddha, M. K. Kocamaz, L. E. Navarro-Serment, and M. Hebert, “Map-supervised road detection,” in Intelligent Vehicles Symposium (IV), 2016 IEEE . IEEE, 2016, pp. 118–123

Original

2016