Abstract
A template updating reasoning engine which can deal with fundamental constraints on the spatial-temporal continuity of target’s motion is proposed. By analyzing target’s continuously adaptive distributions image, a voting method can estimate the tracking window’s scale. In updating phase, by making further computation of likelihood of target model and candidate model, both the model and scale can be automatically updated in time. The tracking ability of KBT can be improved.
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References
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564–577 (2003)
Peng, N., Yang, J., Liu, Z.: Performance analysis for tracking of variable scale objects using mean-shift algorithm. Optical Engineering 44, 7 (2005)
Collins, R.: Mean-shift blob tracking through scale space. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 234–240 (2003)
Yang, J., Peng, N.: Mean-Shift Object Tracking with Automatic Selection of Kernel-Bandwidth. Journal of Software 16(9), 1542–1550 (2005)
Hager, G.D., Dewan, M., Stewart, C.V.: Multiple Kernel Tracking with SSD. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 790–797 (2004)
Fan, Z., Yang, M., Wu, Y.: Multiple collaborative kernel tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1268–1273 (2007)
Comaniciu, D.: An algorithm for data-driven bandwidth selection. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 281–288 (2003)
Han, R., Jing, Z., Li, Y.: Kernel Based Visual Tracking with Scale Invariant Features. Chinese Optics Letters 3, 168 (2008)
Alper, Y., Khurram, S., Mubarak, S.: Target tracking in airborne forward looking infrared imagery. Image and Vision Computing 21, 623–635 (2003)
Bradski, G.R.: Computer vision face tracking as a component of a perceptual user interface. In: Workshop on Applications of Computer Vision, pp. 214–219 (1998)
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Han, R. (2011). Kernel Based Visual Tracking with Reasoning about Adaptive Distribution Image. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_65
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DOI: https://doi.org/10.1007/978-3-642-23896-3_65
Publisher Name: Springer, Berlin, Heidelberg
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