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BM3 E: discriminative density propagation for visual tracking.

Cristian Sminchisescu1, Atul Kanaujia, Dimitris N Metaxas

  • 1Toyota Technological Institute-Chicago, University of Chicago, 1427 East 60th Street, Second Floor, Chicago, IL 60637, USA. crismin@nagoya.uchicago.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 13, 2007
PubMed
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We introduce BM3 E, a Conditional Bayesian Mixture of Experts Markov Model, for consistent probabilistic estimates in discriminative visual tracking. This new model offers improved performance in complex state distribution prediction and human motion reconstruction.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Probabilistic Modeling

Background:

  • Generative models using Kalman or particle filtering are common for visual tracking.
  • Existing methods often require inverting non-linear generative observation models at runtime.
  • There is a need for bottom-up approaches to complement existing top-down models.

Purpose of the Study:

  • To introduce BM3 E, a novel Conditional Bayesian Mixture of Experts Markov Model for discriminative visual tracking.
  • To develop a model capable of consistent probabilistic estimates and temporal inference.
  • To provide a bottom-up alternative to generative models in visual tracking.

Main Methods:

  • Utilizing a Conditional Bayesian Mixture of Experts Markov Model (BM3 E).

Related Experiment Videos

  • Learning to predict complex state distributions directly from image observation descriptors (e.g., histograms, spatial grids).
  • Integrating descriptors into a conditional graphical model for temporal smoothness and uncertainty management.
  • Employing sparsity, mixture modeling, and non-linear dimensionality reduction for efficient computation.

    • Main Results:

    • Established density propagation rules for discriminative inference in continuous, temporal chain models.
    • Developed supervised and unsupervised algorithms for learning feedforward, multivalued contextual mappings.
    • Empirically validated the framework for 3D human motion reconstruction from monocular video.
    • Demonstrated significant performance gains over competing methods.

    Conclusions:

    • BM3 E provides consistent probabilistic estimates for discriminative visual tracking.
    • The model effectively handles temporal and uncertain inference.
    • The framework shows superior performance in 3D human motion reconstruction, outperforming existing methods.