We formulate the problem of 3D human pose estimation and tracking as one of inference in a graphical model. Unlike traditional kinematic tree representations, our model of the body is a collection of loosely-connected body-parts. In particular, we model the body using an undirected graphical model in which nodes correspond to parts and edges to kinematic, penetration, and temporal constraints imposed by the joints and the world. These constraints are encoded using pair-wise statistical distributions, that are learned from motion-capture training data. Human pose and motion estimation is formulated as inference in this graphical model and is solved using Particle Message Passing ( PaMPas ). PaMPas is a form of non-parametric belief propagation that uses a variation of particle filtering that can be applied over a general graphical model with loops. The loose-limbed model and decentralized graph structure allow us to incorporate information from “bottom-up” visual cues, such as limb and head detectors, into the inference process. These detectors enable automatic initialization and aid recovery from transient tracking failures. We illustrate the method by automatically tracking people in multi-view imagery using a set of calibrated cameras and present quantitative evaluation using the HumanEva dataset.

Estimating the 3D orientation of the camera in a video sequence within a global frame of reference is useful for video stabilization when displaying the video in a virtual 3D environment, as well as for accurate navigation and other applications. This task requires the input of orientation sensors attached to the camera to provide absolute 3D orientation in a geographical frame of reference. However, high-frequency noise in the sensor readings makes it impossible to achieve accurate orientation estimates required for visually stable presentation of video sequences that were acquired with a camera subject to jitter, such as a handheld camera or a vehicle mounted camera. On the other hand, image alignment has proven successful for image stabilization, providing accurate frame-to-frame orientation estimates but drifting over time due to error and bias accumulation and lacking absolute orientation. In this paper we propose a practical method for generating high accuracy estimates of the 3D orientation of the camera within a global frame of reference by fusing orientation estimates from an efficient image-based alignment method, and the estimates from an orientation sensor, overcoming the limitations of the component methods.

We present a system for making community driven websites easily accessible from the latest mobile devices. Many of these new devices contain an ensemble of sensors such as cameras, GPS and inertial sensors. We demonstrate how these new sensors can be used to bring the information contained in sites like Wikipedia to users in a much more immersive manner than text or maps. We have collected a large database of images and articles from Wikipedia and show how a user can query this database by simply snapping a photo. Our system uses the location sensors to assist with image matching and the inertial sensors to provide a unique and intuitive user interface for browsing results.

Much of the research on video-based human motion capture assumes the body shape is known a priori and is represented coarsely (e.g. using cylinders or superquadrics to model limbs). These body models stand in sharp contrast to the richly detailed 3D body models used by the graphics community. Here we propose a method for recovering such models directly from images. Specifically, we represent the body using a recently proposed triangulated mesh model called SCAPE which employs a low-dimensional, but detailed, parametric model of shape and pose-dependent deformations that is learned from a database of range scans of human bodies. Previous work showed that the parameters of the SCAPE model could be estimated from marker-based motion capture data. Here we go further to estimate the parameters directly from image data. We define a cost function between image observations and a hypothesized mesh and formulate the problem as optimization over the body shape and pose parameters using stochastic search. Our results show that such rich generative models enable the automatic recovery of detailed human shape and pose from images.

Strong lighting is common in natural scenes yet is often viewed as a nuisance for object pose estimation and tracking. In human shape and pose estimation, cast shadows can be confused with foreground structure while self shadowing and shading variation on the body cause the appearance of the person to change with pose. Rather than attempt to minimize the effects of lighting and shadows, we show that strong lighting in a scene actually makes pose and shape estimation more robust. Additionally, by recovering multiple body poses we are able to automatically estimate the lighting in the scene and the albedo of the body. Our approach makes use of a detailed 3D body model, the parameters of which are directly recovered from image data. We provide a thorough exploration of human pose estimation under strong lighting conditions and show: 1. the estimation of the light source from cast shadows; 2. the estimation of the light source and the albedo of the body from multiple body poses; 3. that a point light and cast shadows on the ground plane can be treated as an additional "shadow camera" that improves pose and shape recovery, particularly in monocular scenes. Additionally we introduce the notion of albedo constancy which employs lighting normalized image data for matching. Our experiments with multiple subjects show that rather than causing problems, strong lighting improves human pose and shape estimation.

Many networked embedded sensing and control systems can be modeled as hybrid systems with interacting continuous and discrete dynamics. These systems present significant challenges for monitoring and diagnosis. Many existing model-based approaches focus on diagnostic reasoning assuming appropriate fault signatures have been generated. However, an important missing piece is the integration of model-based techniques with the acquisition and processing of sensor signals and the modeling of faults to support diagnostic reasoning. This paper addresses key modeling and computational problems at the interface between model-based diagnosis techniques and signature analysis to enable the efficient detection and isolation of incipient and abrupt faults in hybrid systems. A hybrid automata model that parameterizes abrupt and incipient faults is introduced. Based on this model, an approach for diagnoser design is presented. The paper also develops a novel mode estimation algorithm that uses model-based prediction to focus distributed processing signal algorithms. Finally, the paper describes a diagnostic system architecture that integrates the modeling, prediction, and diagnosis components. The implemented architecture is applied to fault diagnosis of a complex electro-mechanical machine, the Xerox DC265 printer, and the experimental results presented validate the approach. A number of design trade-offs that were made to support implementation of the algorithms for online applications are also described.

Many sensing techniques and image processing applications are characterized by noisy, or corrupted, image data. Anisotropic diffusion is a popular, and theoretically well understood, technique for denoising such images. Diffusion approaches however require the selection of an "edge stopping" function, the definition of which is typically ad hoc. We exploit and extend recent work on the statistics of natural images to define principled edge stopping functions for different types of imagery. We consider a variety of anisotropic diffusion schemes and note that they compute spatial derivatives at fixed scales from which we estimate the appropriate algorithm-specific image statistics. Going beyond traditional work on image statistics, we also model the statistics of the eigenvalues of the local structure tensor. Novel edge-stopping functions are derived from these image statistics giving a principled way of formulating anisotropic diffusion problems in which all edge-stopping parameters are learned from training data.

Although most optical flow techniques presume brightness constancy, it is well-known that this constraint is often violated, producing poor estimates of image motion. This paper describes a generalized formulation of optical flow estimation based on models of brightness variations that are caused by time-dependent physical processes. These include changing surface orientation with respect to a directional illuminant, motion of the illuminant, and physical models of heat transport in infrared images. With these models, we simultaneously estimate the 2D image motion and the relevant physical parameters of the brightness change model. The estimation problem is formulated using total least squares, with confidence bounds on the parameters. Experiments in four domains, with both synthetic and natural inputs, show how this formulation produces superior estimates of the 2D image motion.

This paper exploits physical models of time-varying brightness in image sequences to estimate optical flow and physical parameters of the scene. Previous approaches handled violations of brightness constancy with the use of robust statistics or with generalized brightness constancy constraints that allow generic types of contrast and illumination changes. We consider models of brightness variation that have time-dependent physical causes, namely, changing surface orientation with respect to a directional illuminant, motion of the illuminant, and physical models of heat transport in infrared images. We simultaneously estimate the optical flow and the relevant physical parameters. The estimation problem is formulated using total least squares (TLS), with confidence bounds on the parameters.