Abstract
The particle filter has attracted considerable attention in visual tracking due to its relaxation of the linear and Gaussian restrictions in the state space model. It is thus more flexible than the Kalman filter. However, the conventional particle filter uses system transition as the proposal distribution, leading to poor sampling efficiency and poor performance in visual tracking. It is not a trivial task to design satisfactory proposal distributions for the particle filter. In this paper, we introduce an improved particle filtering framework into visual tracking, which combines the unscented Kalman filter and the auxiliary particle filter. The efficient unscented auxiliary particle filter (UAPF) uses the unscented transformation to predict one-step ahead likelihood and produces more reasonable proposal distributions, thus reducing the number of particles required and substantially improving the tracking performance. Experiments on real video sequences demonstrate that the UAPF is computationally efficient and outperforms the conventional particle filter and the auxiliary particle filter.
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References
Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer-Verlag, New York (2001)
Isard, M., Blake, A.: Condensation – Conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–28 (1998)
Cham, T.J., Rehg, J.M.: A multiple hypothesis approach to figure tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, vol. 2, pp. 239–245 (1999)
Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: IEEE Conference on Computer Vision and Pattern Recognition (2000)
Choo, K., Fleet, D.: People tracking using hybrid Monte Carlo filtering. In: IEEE International Conference on Computer Vision, Vancouver, Canada, vol. 2, pp. 321–328 (2001)
Nait-Charif, H., McKenna, S.J.: Tracking poorly modelled motion using particle filters with iterated likelihood weighting. In: Asian Conference on Computer Vision, Jeju Island, Korea (2004)
Vlassis, N., Terwijn, B., Krose, B.: Auxiliary particle filter robot localization from high-dimensional sensor observations. In: IEEE International Conference on Robotics and Automation, vol. 1, pp. 7–12 (2002)
Qian, G., Chellappa, R.: Structure from motion using sequential Monte Carlo methods. International Journal of Computer Vision 59, 5–31 (2004)
Pitt, M.K., Shephard, N.: Filtering via simulation: Auxiliary particle filter. Journal of the American Statistical Association 94, 590–599 (1999)
Chang, C., Ansari, R.: Kernel particle filter: Iterative sampling for efficient visual tracking. In: IEEE International Conference on Image Processing, Barcelona, Spain (2003)
Shan, C., Wei, Y., Tan, T., Ojardias, F.: Real time hand tracking by combining particle filtering and mean shift. In: 6th International Conference on Automatic Face and Gesture Recognition, Seoul, Korea (2004)
Blake, A., North, B., Isard, M.: Learning multi-class dynamics. In: Advances in Neural Information Processing Systems, pp. 389–395. The MIT Press, Cambridge (1999)
Pavlovic, V., Rehg, J., MacCormick, J.: Learning switching linear models of human motion. In: Advances in Neural Information Processing Systems, pp. 981–987. The MIT Press, Cambridge (2000)
Isard, M., Blake, A.: Icondensation: Unifying low-level and high-level tracking in a stochastic framework. In: 5th European Conference Computer Vision, vol. 1, pp. 893–908 (1998)
Doucet, A., Godsill, S., Andrieu, C.: On sequential sampling Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10, 197–208 (2000)
van der Merwe, R., Doucet, A., de Freitas, N., Wan, E.: The unscented particle filter. Technical Report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, UK (2000), available at http://cslu.cse.ogi.edu/nsel/research/ukf.html
Rui, Y., Chen, Y.: Better proposal distributions: Object tracking using unscented particle filter. In: IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, vol. 2, pp. 786–793 (2001)
Li, P., Zhang, T., Pece, A.E.C.: Visual contour tracking based on particle filters. Image and Vision Computing 21, 111–123 (2003)
Andrieu, C., Davy, M., Doucet, A.: Efficient particle filtering for jump Markov systems. application to time-varying autoregressions. IEEE Transactions on Signal Processing 51, 1762–1770 (2003)
Wan, E.A., van der Merwe, R.: The unscented Kalman filter. In: Haykin, S. (ed.) Kalman Filtering and Neural Networks, Wiley Publishing, Chichester (2001)
Julier, S.J., Uhlmann, J.K.: The scaled unscented transformation. In: IEEE American Control Conference, Anchorage AK, USA, pp. 4555–4559. IEEE, Los Alamitos (2002)
Andrieu, C., de Freitas, N., Doucet, A., Jordan, M.I.: An introduction to MCMC for machine learning. Machine Learning 50, 5–43 (2003)
Blake, A., Isard, M.: Active Contours. Spinger, Berlin (1998)
Agarwal, A., Triggs, B.: Tracking articulated motion using a mixture of autoregressive models. In: 8th European Conference on Computer Vision, Prague, Czech Republic, vol. 3 (2004)
Deutscher, J., Davison, A.J., Reid, I.: Automatic partitioning of high dimensional search spaces associated with articulated body motion capture. In: IEEE Conference on Computer Vision and Pattern Recognition (2001)
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Shen, C., van den Hengel, A., Dick, A., Brooks, M.J. (2004). Enhanced Importance Sampling: Unscented Auxiliary Particle Filtering for Visual Tracking. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_17
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DOI: https://doi.org/10.1007/978-3-540-30549-1_17
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