Abstract
We propose a novel approach to tracking objects by low-level line correspondences. In our implementation we show that this approach is usable even when tracking objects with lack of texture, exploiting situations, when feature-based trackers fails due to the aperture problem. Furthermore, we suggest an approach to failure detection and recovery to maintain long-term stability. This is achieved by remembering configurations which lead to good pose estimations and using them later for tracking corrections.
We carried out experiments on several sequences of different types. The proposed tracker proves itself as competitive or superior to state-of-the-art trackers in both standard and low-textured scenes.
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References
Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: Bootstrapping binary classifiers by structural constraints. In: Proc. of CVPR, pp. 49–56 (2010)
Grabner, H., Grabner, M., Bischof, H.: Real-Time Tracking via On-line Boosting. In: Proc. of BMVC (2006)
Cehovin, L., Kristan, M., Leonardis, A.: An adaptive coupled-layer visual model for robust visual tracking. In: Proc. of ICCV (2011)
Matas, J., Vojir, T.: Robustifying the flock of trackers. In: Proc. of Computer Vision Winter Workshop, pp. 91–97 (2011)
Dupac, J., Matas, J.: Ultra-fast tracking based on zero-shift points. In: Proc. of ICASSP, pp. 1429–1432 (2011)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. of Imaging Underst. Workshop, pp. 121–130 (1981)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60, 63–86 (2004)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. of Alvey Vision Conference, pp. 147–151 (1988)
Harris, C., Stennett, C.: Rapid – a video rate object tracker. In: Proc. of BMVC (1990)
Drummond, T., Cipolla, R.: Real-time visual tracking of complex structures. IEEE Trans. PAMI 24, 932–946 (2002)
Tsin, Y., Genc, Y., Zhu, Y., Ramesh, V.: Learn to track edges. In: Proc. of ICCV, pp. 1–8 (2007)
Beveridge, J.R., Riseman, E.M.: How easy is matching 2D line models using local search? (1997)
Chum, O., Matas, J., Kittler, J.: Locally Optimized RANSAC. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 236–243. Springer, Heidelberg (2003)
Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 381–395 (1981)
Hartley, R.I.: Projective reconstruction from line correspondences. In: Proc. of CVPR, pp. 903–907 (1994)
Canny, J.: A computational approach to edge detection. IEEE Trans. PAMI 8, 679–698 (1986)
Olson, C.F., Huttenlocher, D.P.: Automatic target recognition by matching oriented edge pixels. IEEE Trans. Image Processing 6, 103–113 (1997)
Shotton, J., Blake, A., Cipolla, R.: Multiscale categorical object recognition using contour fragments. IEEE Trans. PAMI 30, 1270–1281 (2008)
Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Trans. PAMI 25, 1296–1311 (2003)
Ross, D., Lim, J., Yang, M.-H.: Adaptive Probabilistic Visual Tracking with Incremental Subspace Update. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3022, pp. 470–482. Springer, Heidelberg (2004)
Chen, M., Pang, S.K., Cham, T.J., Goh, A.: Visual tracking with generative template model based on riemannian manifold of covariances. In: Proc. of Int. Conf. on Information Fusion, pp. 874–881 (2011)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2004)
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Lebeda, K., Matas, J., Bowden, R. (2013). Tracking the Untrackable: How to Track When Your Object Is Featureless. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37484-5_29
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DOI: https://doi.org/10.1007/978-3-642-37484-5_29
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