Abstract
Tracking a very actively maneuvering object is challenging due to the lack of state transition dynamics to describe the system’s evolution. In this paper, a coarse-to-fine particle filter algorithm is proposed for such tracking, whereby one loop of the traditional particle filtering approach is divided into two stages. In the coarse stage, the particles adopt a uniform distribution which is parameterized by the limited motion range within each time step. In the following fine stage, the particles are resampled using the results of the coarse stage as the proposal distribution, which incorporates the most present observation. The weighting scheme is implemented using a partitioned color cue that implicitly embeds geometric information to enhance robustness. The system is tested by a publicly available dataset for tracking an intentionally erratic moving human head. The results demonstrate that the proposed system is capable of handling random motion dynamics with a relatively small number of particles.
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References
Arulampalam, M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing 50, 174–188 (2002)
Fieguth, P.: Statistical Image Processing and Multidimensional Modeling, ch. 4, pp. 85–127. Springer, Heidelberg (2010)
Hol, J., Schon, T., Gustafsson, F.: On resampling algorithms for particle filters. In: Nonlinear Statistical Signal Processing Workshop, pp. 79–82 (2006)
Houtekamer, P., Mitchell, H.L.: Data assimilation using an ensemble kalman filter technique. Monthly Weather Review 126, 796–811 (1998)
Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. IEEE Review 92(3), 401–422 (2004)
Kasturi, R., Goldgof, D., Soundararajan, P., Manohar, V., Garofolo, J., Bowers, R., Boonstra, M., Korzhova, V., Zhang, J.: Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol. IEEE Trans. Pattern Analysis and Machine Intelligence 31(2), 319–336 (2009)
Li, Z., Kulić, D.: Particle filter based human motion tracking. In: IEEE International Conference on Control, Automation, Robotics and Vision 2010 (2010)
Nummiaro, K., Koller-Meier, E., Gool, L.V.: An adaptive color-based particle filter. Image and Vision Computing 21(1), 99–110 (2003)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Shafique, K., Shah, M.: A non-iterative greedy algorithm for multi-frame point correspondence. In: Proceeding of IEEE International Conference on Computer Vision (ICCV), pp. 110-115 (2003)
Shen, C., Hengel, A.v.d., Dick, A.: Probabilistic multiple cue intergration for particle filter based tracking. In: Proceeding of VIIth Digital Image Computing: Techniques and Applications, pp. 399–408 (2003)
Sung, H., Choi, K., Cho, S., Byun, H.: Coarse-to-fine particle filter by implicit motion estimation for 3d head tracking on mobile devices. In: 20th International Conference on Pattern Recognition (ICPR 2010), pp. 3615–3618 (2010)
Veenman, C., Reinders, M., Backer, E.: Resolving motion correspondence for densely moving points. IEEE Trans. Patt. Analy. Mach. Intell. 23(1), 54–72 (2001)
Welch, G., Bishop, G.: An introduction to the kalman filter. Technical Report, TR95-041, Computer Science, UNC Chapel Hill (1995)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Comput. Surv. 38(4), 1–45 (2006)
Surveillance performance evaluation initiative (spevi) dataset, http://www.eecs.qmul.ac.uk/~andrea/spevi.html (last accessed: January 2011)
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Miao, YQ., Fieguth, P., Kamel, M.S. (2011). Maneuvering Head Motion Tracking by Coarse-to-Fine Particle Filter. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_39
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DOI: https://doi.org/10.1007/978-3-642-21593-3_39
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