Soumya Hamlaoui
ABSTRACT
Tracking a face and its facial features in a video sequence is a challenging problem in computer vision. In this view, we propose a stochastic tracking system based on a particle- filtering scheme. In this paradigm, the unobserved state includes global face pose and appearance parameters coding both shape and texture information of the face. The adopted observations distribution is derived from an Active Appearance Model (AAM). The transition distribution and the particles number are adaptive in the sense that they are guided by an AAM deterministic search. This optimization stage adjusts the explored area of the state space to the quality of the prediction and enables a substantial gain in computing time. The observation model uses a robust distance measure in order to account for occlusions. Experiments on real video show encouraging results.
- S. Baker and I. Matthews. Lucas-kanade 20 years on: A unifying framework. Int. Journal of Computer Vision, 56(3):221--255, February 2004. Google Scholar
Digital Library
- F. Bettinger, T. F. Cootes, and C. J. Taylor. Modelling facial behaviours. In Proc. BMVC 2002, volume 2, pages 797--806, 2002.Google ScholarCross Ref
- M. Black and A. Jepson. Eigen-tracking: Robust matching and tracking of articulated objects using a view-based representation. Int. Journal of Computer Vision, 36(2): 101--130, 1998. Google ScholarDigital Library
- T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(6):681--685, June 2001. Google ScholarDigital Library
- A. Doucet, J. F. G. De Freitas, and N. Gordon. Sequential Monte Carlo Methods in Practice. Springer-Verlag, 2001.Google ScholarCross Ref
- P. J. Huber. Robust statistics. Wiley, 1981.Google ScholarCross Ref
- M. Isard and A. Blake. Condensation - conditional density propagation for visual tracking. Int. Journal of Computer Vision, 29(1):5--28, 1998. Google ScholarDigital Library
- P. Pérez, C. Hue, J. Vermaak, and M. Gangnet. Colorbased probabilistic tracking. In Proc. Europ. Conf. Computer Vision, pages 661--675, 2002. Google ScholarDigital Library
- D. Ross, J. Lim, and M.-H. Yang. Adaptive probabilistic visual tracking with incremental subspace update. In Proceedings of the European Conference on Computer Vision, 2004.Google ScholarCross Ref
- S. Zhou, R. Chellappa, and B. Moghaddam. Visual tracking and recognition using appearance-adaptive models in particle Iters. IEEE Trans. on Image Processing, To appear, 2004. Google ScholarDigital Library
- Facial action tracking using particle filters and active appearance models
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