Relational Object Tracking

TNT members involved in this project:
Dipl. Math. Oliver Müller
Prof. Dr.-Ing. Bodo Rosenhahn
Prof. Dr.-Ing. Michael Yang
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Object tracking is a substantial task for many computer vision applications such as human-computer interaction, automatic scene analysis, or action recognition.

Single object tracking can take place at different levels of granularity (see Figure 1), ranging from single bounding-box estimates (top), articulated pose estimation with a few parts (center), up to dense deformable models with hundreds of parts (bottom).

Figure 1: Granularity in object tracking

The part-based tracking problem leads to (discrete-)continuous models with a large number of variables and highly non-convex objective functions. Modeling and inference in this domain still remains challenging.

We use Probabilistic Graphical Model representations to solve the part-based object tracking task. Relationships between the parts are described by a factor graph. The (continuous) random variables encode the pose (x,y-location, orientation, and scale) for each part. We use stochastic inference techniques (see below) to solve the challenging non-convex problems.

Paper Tracking Result

Face Tracking Result

Figure 2: Tracking results.

We use a stochastic inference technique to solve the relational feature tracking problem.
As opposed to standard stochastic MRF inference techniques based on Metropolis-Hastings sampling or other heuristic proposal generators, we propose to use an efficient slice sampling approach which leads to a shorter burn-in period without sample rejection (see our slice-sampling paper).

ERC Starting Grants

This project has been partially funded by the ERC within the starting grant Dynamic MinVIP.

  • Conference Contributions
    • Oliver Müller, Bodo Rosenhahn
      Global Consistency Priors for Joint Part-based Object Tracking and Image Segmentation
      IEEE Winter Conference on Applications of Computer Vision (WACV), March 2017
    • Oliver Müller, Michael Y. Yang, Bodo Rosenhahn
      Slice Sampling Particle Belief Propagation
      IEEE International Conference on Computer Vision (ICCV), pp. 1129--1136, Sydney, Australia, 2013