Tracking Complex Objects Using Graphical Object Models

We present a probabilistic framework for component-based automatic detection and tracking of objects in video. We represent objects as spatio-temporal two-layer graphical models, where each node corresponds to an object or component of an object at a given time, and the edges correspond to learned spatial and temporal constraints. Object detection and tracking is formulated as inference over a directed loopy graph, and is solved with non-parametric belief propagation. This type of object model allows object-detection to make use of temporal consistency (over an arbitrarily sized temporal window), and facilitates robust tracking of the object. The two layer structure of the graphical model allows inference over the entire object as well as individual components. AdaBoost detectors are used to define the likelihood and form proposal distributions for components. Proposal distributions provide ‘bottomup’ information that is incorporated into the inference process, enabling automatic ...

• Type: Conference
• Year: 2004
• Where: IWCM
• Authors: Leonid Sigal, Ying Zhu, Dorin Comaniciu, Michael J. Black
• Keywords: Automatic Object Detection, Computer Graphics, Graphical Model, IWCM 2004, Object Detection