Sequential Monte Carlo Tracking of Body Parameters in a Sub-Space
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Online updating appearance generative mixture model for meanshift tracking
Machine Vision and Applications
Bootstrapping sequential monte carlo tracking
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Active shape model based segmentation and tracking of facial regions in color images
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
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This paper describes a probabilistic mode-based multi-hypothesis tracking (MHT) algorithm. The modes are the local maximums refined from initial samples in a parametric state space. Because the modes are highly representative, this technique allows us to use a small number of hypotheses to effectively model non-linear probabilistic distributions. To ensure real-time tracking performance, we propose a novel parametric causal contour model and an efficient dynamic programming scheme to refine the initial contours to nearby modes. Furthermore, to overcome the common drawback of conventional MHT techniques, i.e., producing only the maximum likelihood estimates instead of the desired posterior, we introduce the highly effective importance sampling framework into MHT, and develop a novel procedure to estimate the posterior from the importance function. Experiments on a challenging real-world video sequence demonstrate that the proposed tracking technique is both robust in complex environment (e.g., clutter background and partial occlusion) and efficient in computation.