Abstract
This paper presents a novel efficient and robust direct visual tracking method under illumination variations. In our approach, non-Euclidean Lie group characteristics of both geometric and photometric transformations are exploited. These transformations form Lie groups and are parameterized by their corresponding Lie algebras. By applying the efficient second-order minimization trick, we derive an efficient second-order optimization technique for jointly solving the geometric and photometric parameters. Our approach has a high convergence rate and low iterations. Moreover, our approach is almost not affected by linear illumination variations. The superiority of our proposed method over the existing direct methods, in terms of efficiency and robustness is demonstrated through experiments on synthetic and real data.
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References
Benhimane, S., Malis, E.: Homography-based 2D visual tracking and servoing. Int. J. Robot. Res. 26(7), 661–676 (2007)
Baker, S., Matthews, I.: Lucas-Kanade 20 years on: a unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004)
Dame, A., Marchand, E.: Second-order optimization of mutual information for real-time image registration. IEEE Trans. Image Process. 21(9), 4190–4203 (2012)
Evangelidis, G.D., Psarakis, E.Z.: Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1858–1865 (2008)
Richa, R., et al.: Visual tracking using the sum of conditional variance. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2953–2958 (2011)
Alismail, H., Browning, B., Lucey, S.: Robust tracking in low light and sudden illumination changes. In: Proceedings of 2016 Fourth International Conference on 3D Vision (3DV), pp. 389–398 (2016)
Luong, H.Q., et al.: Joint photometric and geometric image registration in the total least square sense. Pattern Recogn. Lett. 32(15), 2061–2067 (2011)
Silveira, G., Malis, E.: Unified direct visual tracking of rigid and deformable surfaces under generic illumination changes in grayscale and color images. Int. J. Comput. Vis. 89(1), 84–105 (2010)
Gouiffes, M., et al.: A study on local photometric models and their application to robust tracking. Comput. Vis. Image Underst. 116(8), 896–907 (2012)
Fouad, M.M., Dansereau, R.M., Whitehead, A.D.: Image registration under illumination variations using region-based confidence weighted m-estimators. IEEE Trans. Image Process. 21(3), 1046–1060 (2012)
Silveira, G., Malis, E.: Real-time visual tracking under arbitrary illumination changes. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, vol. 1–8, pp. 1–6 (2007)
Bartoli, A.: Groupwise geometric and photometric direct image registration. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2098–2108 (2008)
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Li, C., Shi, Z., Liu, Y., Liu, T. (2017). Joint Geometric and Photometric Visual Tracking Based on Lie Group. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2017. Lecture Notes in Computer Science(), vol 10589. Springer, Cham. https://doi.org/10.1007/978-3-319-68445-1_34
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DOI: https://doi.org/10.1007/978-3-319-68445-1_34
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