[34] Eero P Simoncelli and Bruno A Olshausen. Natural image statistics and neural representation. Annual review of neuroscience, 24(1):1193-1216, 2001. 3, 4
[35] Elizabeth S Spelke, Peter Vishton, and Claes Von Hofsten. Object perception, object-directed action, and physical knowledge in infancy." 1995. 3
[36] James J Gibson. The perception of the visual world. 1950. 3
[37] Berthold KP Horn and Brian G Schunck. Determining optical flow. Artificial intelligence, 17 (1-3):185-203, 1981.
[38] Bruce D Lucas and Takeo Kanade. An iterative image registration technique with an application to stereo vision. In *IJCAI'81: 7th international joint conference on Artificial intelligence*, volume 2, pages 674-679, 1981.
[39] Thomas Brox, Andrés Bruhn, Nils Papenberg, and Joachim Weickert. High accuracy optical flow estimation based on a theory for warping. In Computer Vision-ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part IV 8, pages 25-36. Springer, 2004.
[40] Deqing Sun, Stefan Roth, and Michael J Black. Secrets of optical flow estimation and their principles. In 2010 IEEE computer society conference on computer vision and pattern recognition, pages 2432-2439. IEEE, 2010.
[41] Ce Liu, Jenny Yuen, and Antonio Torralba. Sift flow: Dense correspondence across scenes and its applications. IEEE transactions on pattern analysis and machine intelligence, 33(5): 978-994, 2010.
[42] Moritz Menze, Christian Heipke, and Andreas Geiger. Discrete optimization for optical flow. In Pattern Recognition: 37th German Conference, GCPR 2015, Aachen, Germany, October 7-10, 2015, Proceedings 37, pages 16-28. Springer, 2015. 3
[43] Jia Xu, René Ranftl, and Vladlen Koltun. Accurate optical flow via direct cost volume processing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1289-1297, 2017. 3
[44] Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8934-8943, 2018. 3
[45] D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black. A naturalistic open source movie for optical flow evaluation. In A. Fitzgibbon et al. (Eds.), editor, European Conf. on Computer Vision (ECCV), Part IV, LNCS 7577, pages 611-625. Springer-Verlag, October 2012. 3
[46] Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11):1231-1237, 2013. 3
[47] Ishwar K Sethi and Ramesh Jain. Finding trajectories of feature points in a monocular image sequence. IEEE Transactions on pattern analysis and machine intelligence, (1):56-73, 1987. 3
[48] Dorin Comaniciu, Visvanathan Ramesh, and Peter Meer. Kernel-based object tracking. IEEE Transactions on pattern analysis and machine intelligence, 25(5):564-577, 2003.
[49] Changjiang Yang, Ramani Duraiswami, and Larry Davis. Efficient mean-shift tracking via a new similarity measure. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), volume 1, pages 176-183. IEEE, 2005.
[50] Mykhaylo Andriluka, Stefan Roth, and Bernt Schiele. People-tracking-by-detection and people-detection-by-tracking. In 2008 IEEE Conference on computer vision and pattern recognition, pages 1-8. IEEE, 2008.
[51] Zdenek Kalal, Krystian Mikolajczyk, and Jiri Matas. Tracking-learning-detection. IEEE transactions on pattern analysis and machine intelligence, 34(7):1409-1422, 2011.