Abstract
Effective appearance models are one critical factor for robust object tracking. In this paper, we introduce foreground feature salience concept into the background modelling, and put forward a novel foreground salience-based corrected background weighted-histogram (FS-CBWH) scheme for object representation and tracking, which exploits salient features of both foreground and background. We think that background and foreground salient features are both crucial for object representation and tracking. Experimental results show that the proposed FS-CBWH scheme can improve the robustness and performance of mean-shift tracker significantly especially in heavy occlusions and large background variation scenes.
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Bradski, G.R.: Real time face and object tracking as a component of a perceptual user interface. In: Fourth IEEE Workshop on Applications of Computer Vision, pp. 214–219. IEEE Press, New York (1998)
Papanikolopoulos, N.P., Khosla, P.K., Kanade, T.: Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision. IEEE Trans. Robotics and Automation 9(1), 14–35 (1993)
Stauffer, C., Grimson, W.E.L.: Learning Patterns of Activity Using Real-Time Tracking. IEEE Trans. Pattern Anal. and Mach. Intell. 22(8), 747–757 (2000)
Devi, M.S., Bajaj, P.R.: Active Facial Tracking. In: 3rd International Conference on Emerging Trends in Engin. and Tech., pp. 91–95. IEEE Press, New York (2010)
Isard, M., Blake, A.: CONDENSATION—Conditional Density Propagation for Visual Tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-Based Probabilistic Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)
Vojir, T., Noskova, J., Matas, J.: Robust Scale-Adaptive Mean-Shift for Tracking. In: Kämäräinen, J.-K., Koskela, M. (eds.) SCIA 2013. LNCS, vol. 7944, pp. 652–663. Springer, Heidelberg (2013)
Ning, J.F., Zhang, L., Zhang, D., Wu, C.: Robust mean-shift tracking with corrected background-weighted histogram. IET Comput. Vision 6(1), 62–69 (2010)
Wang, L.F., Pan, C.H., Xiang, S.M.: Mean-shift tracking algorithm with weight fusion strategy. In: 18th Inter. Conf. on Image Proc., pp. 473–476. IEEE Press, New York (2011)
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 798–805. IEEE Press, New York (2006)
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Wang, D., Shi, Y., Sun, W., Yu, S. (2014). Object Tracking with a Novel Method Based on FS-CBWH within Mean-Shift Framework. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_56
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DOI: https://doi.org/10.1007/978-3-319-12436-0_56
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