Fetching the paper…
Reading the bibliography…
This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments.
T.-D. Vu, O. Aycard, and N. Appenrodt, “Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments,” in Intelligent Vehicles Symposium, 2007 IEEE , June 2007, pp. 190–195
2007
Earlier work this paper cites.
A. Petrovskaya and S. Thrun, “Model based vehicle detection and tracking for autonomous urban driving,” Autonomous Robots , vol. 26, no. 2, pp. 123–139, 2009
2009
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 , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
G. E. Dahl, D. Yu, L. Deng, and A. Acero, “Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition,” Audio, Speech, and Language Processing, IEEE Transactions on , vol. 20, no. 1, pp. 30–42, 2012
2012
Earlier work this paper cites.
T. Wang, D. J. Wu, A. Coates, and A. Y. Ng, “End-to-end text recognition with convolutional neural networks,” in Pattern Recognition (ICPR), 2012 21st International Conference on . IEEE, 2012, pp. 3304–3308
2012
Earlier work this paper cites.
2014
Cited alongside, same era.
D. Z. Wang, I. Posner, and P. Newman, “Model-free detection and tracking of dynamic objects with 2d lidar,” The International Journal of Robotics Research , vol. 34, no. 7, pp. 1039–1063, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
P. Ondrúška and I. Posner, “Deep tracking: Seeing beyond seeing using recurrent neural networks,” in The Thirtieth AAAI Conference on Artificial Intelligence (AAAI) , Phoenix, Arizona USA, February 2016
2016
Closest in time.
2016
Closest in time.
2016
Closest in time.
M. Jaderberg, K. Simonyan, A. Zisserman, et al. , “Spatial transformer networks,” in Advances in Neural Information Processing Systems , 2015, pp. 2017–2025
2025
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Xingjian, 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 , 2015, pp. 802–810
2015
Cited alongside, same era.
S. Choi, K. Lee, and S. Oh, “Robust modeling and prediction in dynamic environments using recurrent flow networks.”
Cited in the paper.
M. Oquab, “Module for spatial transformer networks,” https://github.com/qassemoquab/stnbhwd/
Cited in the paper.