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We introduce BM?E, a Conditional Bayesian Mixture of Experts Markov Model, for consistent probabilistic estimates in discriminative visual tracking. The model applies to problems of temporal and uncertain inference and represents the unexplored bottom-up counterpart of pervasive generative models estimated with Kalman filtering or particle filtering. Instead of inverting a non-linear generative observation model at run-time, we learn to cooperatively predict complex state distributions directly from descriptors that encode image observations — typically bag-of-feature global image histograms or descriptors computed over regular spatial grids. These are integrated in a conditional graphical model in order to enforce temporal smoothness constraints and allow a principled management of uncertainty. The algorithms combine sparsity, mixture modeling, and non-linear dimensionality reduction for efficient computation in high-dimensional continuous state spaces. The combined system automatically self-initializes and recovers from failure. The research has three contributions: (1) We establish the density propagation rules for discriminative inference in continuous, temporal chain models; (2) We propose flexible supervised and unsupervised algorithms for learning feedforward, multivalued contextual mappings (multimodal state distributions) based on compact, conditional Bayesian mixture of experts models; (3) We validate the framework empirically for the reconstruction of 3d human motion in monocular video sequences. Our tests on both real and motion capture-based sequences show significant performance gains with respect to competing nearest-neighbor, regression, and structured prediction methods.
computer vision, statistical models, video analysis, motion, tracking
Dimitris N. Metaxas, Cristian Sminchisescu, Atul Kanaujia, "BM?E : Discriminative Density Propagation for Visual Tracking", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 2030-2044, November 2007, doi:10.1109/TPAMI.2007.1111
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