Abstract
Tracking objects in image sequences involves performing motion analysis at the object level, which is becoming an increasingly important technology in a wide range of computer video applications, including video teleconferencing, security and surveillance, video segmentation, and editing. In this chapter, we focus on sequential Bayesian estimation techniques for visual tracking. We first introduce the sequential Bayesian estimation framework, which acts as the theoretic basis for visual tracking. Then, we present approaches to constructing representation models for specific objects.
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References
Birkhoff G, de Boor C, Swartz B, Wendroff B (1966) Rayleigh-Ritz approximation by piecewise cubic polynomials. SIAM Journal on Numerical Analysis 3:188
Blake A, Curwen R, Zisserman A (1993) A framework for spatiotemporal control in the tracking of visual contours. International Journal of Computer Vision 11(2):127–145
Blake A, Isard M, Reynard D (1995) Learning to track the visual motion of contours. Artificial Intelligence 78(1–2):179–212
Doucet A, Gordon N, Kroshnamurthy V (2001) Particle filters for state estimation of jump Markov linear systems. IEEE Transactions on Signal Processing 49(3):613–624
Elgammal A, Duraiswami R, Harwood D, Davis L (2002) Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE 90(7):1151–1163
Egammal A, Duraiswami R, Davis L (2003) Efficient Kernel density estimation using the fast gauss transform with applications to color modeling and tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence pp 1499–1504
Gordon N, Salmond D, Smith A (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In: IEE Proceedings of Radar and Signal Processing, vol 140, pp 107–113
Greengard L, Strain J (1991) The fast Gauss transform. SIAM Journal on Scientific and Statistical Computing 12(1):79–94
Isard M, Blake A (1998) Condensation conditional density propagation for visual tracking. International Journal of Computer Vision 29(1):5–28
Liu J (2001) Monte Carlo Strategies in Scientific Computing. Springer, New York
Liu J, Chen R, Wong W (1999) Rejection control and importance sampling.Journal of the American Statistical Association 93:1022–1301
Moghaddam B, Pentland A (1997) Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7):696–710
Phillips P, Wechsler H, Huang J, Rauss P (1998) The FERET database and evaluation procedure for face-recognition algorithms. Image and Vision Computing 16(5):295–306
Zhang J, Yan Y, Lades M (1997) Face recognition: Eigenface, elastic matching, and neural nets: Automated biometrics. Proceedings of the IEEE 85(9):1423–1435
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© 2009 Springer-Verlag London Limited
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Zheng, N., Xue, J. (2009). Bayesian Tracking of Visual Objects. In: Statistical Learning and Pattern Analysis for Image and Video Processing. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-312-9_8
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DOI: https://doi.org/10.1007/978-1-84882-312-9_8
Publisher Name: Springer, London
Print ISBN: 978-1-84882-311-2
Online ISBN: 978-1-84882-312-9
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