Abstract
In this paper, a robust visual tracking method is proposed to track an object in dynamic conditions that include motion blur, illumination changes, pose variations, and occlusions. To cope with these challenges, multiple trackers with different feature descriptors are utilized, and each of which shows different level of robustness to certain changes in an object’s appearance. To fuse these independent trackers, we propose two configurations, tracker selection and interaction. The tracker interaction is achieved based on a transition probability matrix (TPM) in a probabilistic manner. The tracker selection extracts one tracking result from among multiple tracker outputs by choosing the tracker that has the highest tracker probability. According to various changes in an object’s appearance, the TPM and tracker probability are updated in a recursive Bayesian form by evaluating each tracker’s reliability, which is measured by a robust tracker likelihood function (TLF). When the tracking in each frame is completed, the estimated object’s state is obtained and fed into the reference update via the proposed learning strategy, which retains the robustness and adaptability of the TLF and multiple trackers. The experimental results demonstrate that our proposed method is robust in various benchmark scenarios.
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Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. IJCV 29(1), 5–28 (1998)
Jilkov, V.P., Li, X.R.: Online Bayesian estimation of transition probabilities for markovian jump systems. IEEE Transactions on Signal Processing 52(6), 307–315 (2004)
Ross, D., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. IJCV 77, 125–141 (2008)
Santner, J., Leistner, C., Saffari, A., Pock, T., Bischof, H.: Prost: Parallel robust online simple tracking. In: CVPR, pp. 723–730 (2010)
Spengler, M., Schiele, B.: Towards robust multi-cue integration for visual tracking. Machine Vision and Applications 14(1), 50–58 (2003)
Giebel, J., Gavrila, D.M., Schnörr, C.: A Bayesian Framework for Multi-cue 3D Object Tracking. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 241–252. Springer, Heidelberg (2004)
Brasnett, P., Mihaylova, L., Canagarajah, N., Mihaylova, L., Canagarajah, N., Bull, D.: Particle filtering with multiple cues For object tracking. In: Proc. of SPIE’s Annual Symp. EI ST, pp. 430–441 (2005)
Wang, H., Suter, D.: Efficient visual tracking by probabilistic fusion of multiple cues. In: International Conference on Pattern Recognition, pp. 892–895 (2006)
Leichter, I., Lindenbaum, M., Rivlin, E.: A general framework for combining visual trackers - the ”black boxes” approach. IJCV 67(3), 343–363 (2006)
Badrinarayanan, V., Perez, P., Clerc, F.L., Oisel, L.: Probabilistic color and adaptive multi-feature tracking with dynamically switched priority between cues. In: ICCV, pp. 1–8 (2007)
Du, W., Piater, J.: A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 225–238. Springer, Heidelberg (2008)
Moreno-Noguer, F., Sanfeliu, A., Samaras, D.: Dependent multiple cue integration for robust tracking. PAMI 30(4), 670–685 (2008)
Stenger, B., Woodley, T., Cipolla, R.: Learning to track with multiple observers. In: CVPR, pp. 2647–2654 (2009)
Zelniker, E.E., Hospedales, T.M., Gong, S., Xiang, T.: A unified Bayesian framework for adaptive visual tracking. In: BMVC, pp. 18.1–18.11 (2009)
Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR, pp. 1269–1276 (2010)
Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: ICCV, pp. 1436–1443 (2009)
Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with applications to tracking and navigation. Wiley, New York (2001)
Mei, X., Ling, H., Wu, Y., Blasch, E., Bai, L.: Minimum error bounded efficient l1 tracker with occlusion detection. In: CVPR, pp. 1257–1264 (2011)
Kim, S.-J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large-scale l1 regularized least squares. IEEE Journal on Selected Topics in Signal Processing 1(4), 606–617 (2007)
Yang, M.-H.: Face detection. In: Encyclopedia of Biometrics, pp. 303–308 (2009)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Computing Surveys 38(4) (2006)
Cifuentes, C.G., Sturzel, M., Jurie, F., Brostow, G.J.: Motion models that only work sometimes. In: BMVC (2012)
Everingham, M., Van Gool, L.J., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV 88(2), 303–338 (2010)
Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10(3), 197–208 (2000)
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR, pp. 798–805 (2006)
Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR, pp. 983–990 (2009)
Kalal, Z., Matas, J., Mikolajczyk, K.: P-n learning: bootstrapping binary classifiers by structural constraints. In: CVPR, pp. 49–56 (2010)
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Yoon, J.H., Kim, D.Y., Yoon, KJ. (2012). Visual Tracking via Adaptive Tracker Selection with Multiple Features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33765-9_3
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DOI: https://doi.org/10.1007/978-3-642-33765-9_3
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