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DOI: 10.14489/vkit.2020.08.pp.012-018
Бойцова Э. П., Лебедев И. С.
МЕТОД ВЫЧИСЛЕНИЯ ПЛОТНОГО ОПТИЧЕСКОГО ПОТОКА НА ОСНОВЕ АУГМЕНТАЦИИ ХАРАКТЕРИСТИЧЕСКИХ ТОЧЕК
(с. 12-18)
Аннотация. Предложен метод вычисления плотного оптического потока на основе устойчивого оптического потока RLOF (Robust Local Optical Flow). Для улучшения качественных показателей использован AGAST-детектор (Adaptive and Generic Accelerated Segment Test) с аугментацией характеристических точек. Его применение позволило сократить время вычисления оптического потока без потери качества результата. Проведена денсификация потока методом растровой интерполяции, где геодезические веса заменены весами на основе вторых частных производных. Применено вариационное уточнение для финальной обработки оптического потока.
Ключевые слова: оптический поток; детекторы ключевых точек; устойчивый локальный оптический поток; денсификация оптического потока; оценка больших смещений; инициализация по глобальному движению.
Boytsova E. P., Lebedev I. S.
METHOD OF OPTICAL FLOW CALCULATION BASED ON FEATURE AUGMENTATION
(pp. 12-18)
Abstract. The paper addresses the problem of robust optical flow computation for real-time applications. We present an efficient, in presence of varying illumination and large displacements, method of dense optical flow calculation based on Robust Local Optical Flow (RLOF). Introduction of Adaptive and Generic Accelerated Segment Test (AGAST) feature detector with additional augmentation by simple grid points provides a stable and reliable under difficult conditions set of features and uniform points that produces dense and accurate optical flow and leads to better performance. The usage of feature points for prior initialization leads for robustness for large displacements as well. The other essential step of optical flow calculation – densification of optical flow – was strengthen by modification of weights. The raster interpolation was applied with weights on second derivatives to make more edgesensitive optical flow. To enhance the overall result, a post-processing step of variational refinement process were added to optical flow calculation framework. The proposed method was evaluated on Max Planck Institute (MPI) Sintel dataset – a challenging set of 3D-animated movies with large range of motions and illuminations, which has ground truth optical flow available. The comparison with initial RLOF method were measured by running time and quality parameters, measured by End Point Error (EPE) and Angular Error (AE). The evaluation shows the decrease of running time by approximately 40 % and improvement of EPE and AE, that demonstrates the effectiveness of the method.
Keywords: Optical flow; Feature detectors; Robust local optical flow; Optical flow densification; Large motion estimation; Global motion prior initialization.
Э. П. Бойцова, И. С. Лебедев (Санкт-Петербургский институт информатики и автоматизации Российской академии наук, Санкт-Петербург, Россия) E-mail:
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E. P. Boytsova, I. S. Lebedev (St. Petersburg Institute for Informatics and Automation of Russian Academy of Sciences, St. Petersburg, Russia) E-mail:
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1. Lucas B., Kanade T. (1981). An Iterative Image Registration Technique with an Application to Stereo Vision. International Joint Conference on Artificial Intelligence (IJCAI), Vol. 81, pp. 674 – 679.
2. Tomasi C., Kanade T. (1991). Detection and Tracking of Point Features. International Journal of Computer Vision, Vol. 9, pp. 185 – 203.
3. Bouguet J.-Y. (2000). Pyramidal Implementation of the Lucas-Kanade Feature Tracker Description of the Algorithm. Intel Corporation, Microprocessor Research Labs.
4. Horn B., Schunck B. (1981). Determining Optical Flow. International Joint Conference on Artificial Intelligence, Vol. 17, pp. 185 – 203.
5. Kroeger T. et al. (2016). Fast Optical Flow Using Dense Inverse Search. ECCV 2016. Lecture Notes in Computer Science, Vol. 9908, pp. 471 – 488.
6. Brox T. et al. (2004). High Accuracy Optical Flow Estimation Based on a Theory for Warping. Proceedings of the European Conference on Computer Vision (ECCV), Vol. 3024, pp. 25 – 36.
7. Revaud J. et al. (2015). EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1164 – 1172.
8. Revaud J. et al. (2016). Deep Matching: Hierarchical Deformable Dense Matching. International Journal of Computer Vision, Vol. 120, (3), pp. 300 – 323.
9. Geistert J., Senst T., Sikora T. (2016). Robust Local Optical Flow: Dense Motion Vector Feld Interpolation. Picture Coding Symposium (PCS), pp. 1 – 5.
10. McCane B. et al. (2001). On Benchmarking Optical Flow. Computer Vision and Image Understanding, Vol. 84, pp. 126 – 143.
11. Butler D. J. et al. (2012). A Naturalistic Open Source Movie for Optical Flow Evaluation. ECCV. Lecture Notes in Computer Science, Vol. 7577, pp. 611 – 625.
12. Senst T., Eiselein V., Sikora T. (2012). Robust Local Optical Flow for Feature Tracking. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 22, pp. 1377 – 1387.
13. Senst T. et al. (2014). Cross Based Robust Local Optical Flow. IEEE International Conference on Image Processing (ICIP’2014), pp. 1967 – 1971.
14. Senst T., Geistert J., Sikora T. (2016). Robust Local Optical Flow: Long-Range Motions and Varying Illuminations. IEEE International Conference on Image Processing (ICIP), pp. 4478 – 4482.
15. Mair E. et al. (2010). Adaptive and Generic Corner Detection Based on the Accelerated Segment Test. ECCV 2010. Lecture Notes in Computer Science, Vol. 6312, pp. 183 – 196.
16. Criminisi A. et al. (2010). Geodesic Image and Video Editing. ACM Transactions on Graphics, Vol. 29, (5), pp. 134:1 – 134:15.
17. Min D. et al. (2014). Fast Global Image Smoothing Based on Weighted Least Squares. IEEE Transactions on Image Processing, Vol. 23, (12), pp. 5638 – 5653.
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