Leonid Sigal (ls -at- cs.brown.edu)
Brown University

Vendredi  20 Avril, 16h, Amphi Rataud

Title: Continuous-State Graphical Models for Object Localization, Pose
Estimation, and Tracking


Abstract: Reasoning about pose and motion of objects, based on images
or video, is an important task for many machine vision
applications. Estimating the pose of articulated objects such as
people and animals is particularly challenging due to the complexity
of the possible poses yet has applications in computer vision,
medicine, biology, animation, and entertainment. Realistic natural
scenes, object motion, noise in the image observations, incomplete
evidence that arises from occlusions, and high dimensionality of the
pose itself are all challenges that need to be addressed.

In this talk a class of approaches that model objects using
hierarchical continuous-state graphical models will be
presented. These approaches can be used to effectively model complex
objects by allowing tractable and robust inference algorithms that are
able to infer pose of these objects in the presence of realistic
appearance variations and articulations. In these models that can be
used to model both rigid and articulated object structures, nodes
correspond to parts of objects and edges represent the constraints
between parts encoded as statistical distributions. For articulated
objects, these constraints can model spatial, temporal and occlusion
relationships between parts. Localization, pose estimation, and
tracking can then be formulated as inference in these graphical
models. This formulation has a number of advantages over more
traditional methods and can be used to solve the challenging problem
of inferring the 3D pose of the person from single monocular image.