References
- Lee, Byeong-Yun. "국내외 자율주행자동차 기술개발 동향과 전망." Information and Communications Magazine 33.4 (2016): 10-16.
- 강승준, "AI식별추적시스템구축 사업 의의와 성과", 정보통신산업진흥원 이슈리포트 제 20호 pp.4-5, 2020
- Deo, Nachiket, and Mohan M. Trivedi. "Learning and predicting on-road pedestrian behavior around vehicles." 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017.
- Baqui, Muhammad, and Rainald Lohner. "Real-time crowd safety and comfort management from CCTV images." Real-Time Image and Video Processing 2017. Vol. 10223. International Society for Optics and Photonics, 2017.
- Fortun, Denis, Patrick Bouthemy, and Charles Kervrann. "Optical flow modeling and computation: A survey." Computer Vision and Image Understanding 134 (2015): 1-21. https://doi.org/10.1016/j.cviu.2015.02.008
- Horn, Berthold KP, and Brian G. Schunck. "Determining optical flow." Artificial intelligence 17.1-3 (1981): 185-203. https://doi.org/10.1016/0004-3702(81)90024-2
- Czirok, Andras, et al. "Optical-flow based non-invasive analysis of cardiomyocyte contractility." Scientific reports 7.1 (2017): 1-11. https://doi.org/10.1038/s41598-016-0028-x
- Nam, Tae-Jin, Rae-Hong Park, and Jae-Ho Yun. "Optical flow based frame interpolation of ultrasound images." International Conference Image Analysis and Recognition. Springer, Berlin, Heidelberg, 2006.
- Schmoderer, Timothee, et al. "Learning optical flow for fast MRI reconstruction." Inverse Problems (2021).
- 김영민, et al. "딥러닝과 Optical Flow 를 활용한 보행자 사고 방지 모델." 한국정보과학회 학술발표논문집 (2021): 1690-1692.
- Rateke, Thiago, and Aldo von Wangenheim. "Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data." Machine Vision and Applications 31.7 (2020): 1-11. https://doi.org/10.1007/s00138-019-01050-8
- 김지혜, et al. "Optical Flow 기반 CCTV 영상에서의 차량 통행량 및 통행 속도 추정에 관한 연구." 방송공학회논문지 22.4 (2017): 448-461. https://doi.org/10.5909/JBE.2017.22.4.448
- 권언혜, 노승종, and 전문구. "옵티컬 플로우 기반 장면 모델링을 통한 교통 영상 내의 이상 상황 인식 시스템." 한국정보처리학회 학술대회논문집 19.2 (2012): 488-491.
- 백종환, and 김상훈. "드론과 지상로봇 간의 협업을 위한 광학흐름 기반 마커 추적방법." 정보처리학회논문지. 소프트웨어 및 데이터 공학 7.3 (2018): 107-112.
- Urieva, Natallia, et al. "Collision Detection and Avoidance using Optical Flow for Multicopter UAVs." 2020 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2020.
- Lucas, Bruce D., and Takeo Kanade. "An iterative image registration technique with an application to stereo vision." 1981.
- Bouguet, Jean-Yves. "Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm." Intel corporation 5.1-10 (2001): 4.
- Farneback, Gunnar. "Two-frame motion estimation based on polynomial expansion." Scandinavian conference on Image analysis. Springer, Berlin, Heidelberg, 2003.
- Shi, Jianbo. "Good features to track." 1994 Proceedings of IEEE conference on computer vision and pattern recognition. IEEE, 1994.
- Dosovitskiy, Alexey, et al. "Flownet: Learning optical flow with convolutional networks." Proceedings of the IEEE international conference on computer vision. 2015.
- Baker, Simon, et al. "A database and evaluation methodology for optical flow." International journal of computer vision 92.1 (2011): 1-31. https://doi.org/10.1007/s11263-010-0390-2
- Butler, Daniel J., et al. "A naturalistic open source movie for optical flow evaluation." European conference on computer vision. Springer, Berlin, Heidelberg, 2012.
- Geiger, Andreas, et al. "Vision meets robotics: The kitti dataset." The International Journal of Robotics Research 32.11 (2013): 1231-1237. https://doi.org/10.1177/0278364913491297
- Aubry, Mathieu, et al. "Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014
- Mayer, Nikolaus, et al. "A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Richter, Stephan R., Zeeshan Hayder, and Vladlen Koltun. "Playing for benchmarks." Proceedings of the IEEE International Conference on Computer Vision. 2017.
- Ilg, Eddy, et al. "Flownet 2.0: Evolution of optical flow estimation with deep networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
- Hui, Tak-Wai, Xiaoou Tang, and Chen Change Loy. "Liteflownet: A lightweight convolutional neural network for optical flow estimation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
- Sun, Deqing, et al. "Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
- Ranjan, Anurag, and Michael J. Black. "Optical flow estimation using a spatial pyramid network." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
- Brox, Thomas, et al. "High accuracy optical flow estimation based on a theory for warping." European conference on computer vision. Springer, Berlin, Heidelberg, 2004.
- Hu, Yinlin, Rui Song, and Yunsong Li. "Efficient coarse-to-fine patchmatch for large displacement optical flow." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
- Teed, Zachary, and Jia Deng. "Raft: Recurrent all-pairs field transforms for optical flow." European conference on computer vision. Springer, Cham, 2020.
- Chung, Junyoung, et al. "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint arXiv:1412.3555 (2014).
- Ash, Jordan T., and Ryan P. Adams. "On warm-starting neural network training." arXiv preprint arXiv:1910.08475 (2019).
- Jiang, Shihao, et al. "Learning to Estimate Hidden Motions with Global Motion Aggregation." arXiv preprint arXiv:2104.02409 (2021).
- Sudheendra Vijayanarasimhan, Susanna Ricco, Cordelia Schmid, Rahul Sukthankar, and Katerina Fragkiadaki. Sfm-net: Learning of structure and motion from video. arXiv preprint arXiv:1704.07804, 2017.
- Zhao, Shengyu, et al. "Maskflownet: Asymmetric feature matching with learnable occlusion mask." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.
- Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems. 2017.
- Revaud, Jerome, et al. "Epicflow: Edge-preserving interpolation of correspondences for optical flow." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
- Revaud, Jerome, et al. "Deepmatching: Hierarchical deformable dense matching." International Journal of Computer Vision 120.3 (2016): 300-323. https://doi.org/10.1007/s11263-016-0908-3
- Royer, Loic A., et al. "Convexity shape constraints for image segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
- Tekin, Bugra, et al. "Learning to fuse 2d and 3d image cues for monocular body pose estimation." Proceedings of the IEEE International Conference on Computer Vision. 2017.
- Wulff, Jonas, Laura Sevilla-Lara, and Michael J. Black. "Optical flow in mostly rigid scenes." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.