"Continuous-state Graphical Models for Object Localization, Pose Estimation and Tracking"

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 thesis we propose a class of approaches that model objects using continuous-state graphical models. We show that 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. We use continuous-state graphical models to model both rigid and articulated object structures; where nodes correspond to parts of objects and edges represent the constraints between parts encoded as statistical distributions. For rigid objects, these constraints can model spatial and temporal relationships between parts; for articulated objects kinematic, inter-penetration and occlusion relationships. Localization, pose estimation, and tracking can then be formulated as inference in these graphical models. This has a number of advantages over more traditional methods. First, these models allow inference algorithms that scale linearly with the number of body parts by breaking up the high-dimensional search for pose into a number of lower-dimensional collaborative searches. Secondly, partial occlusions can be dealt with robustly by propagating spatial information between parts. Thirdly, "bottom-up" information can be incorporated directly and effectively into the inference process, helping the algorithm to recover from transient tracking failures. We show that these hierarchical continuous-state graphical models can be used to solve the challenging problem of inferring the 3D pose of the person from a single monocular image.

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