Research |
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As you might have noticed this website is not being updated a great deal. This is because the pages are being moved to my new website. The move will be completed, uh, um, eventually. Current researchMy current work is on Markerless Motion Capture. Motion capture methods traditionally use active or passive markers, and there exist applications where it is desirable to do away with markers for a variety of reasons not the least of which is their invasive nature. In particular, biomechanical and clinical applications, where marker-based motion capture is the state-of-the-art technique would benefit greatly from such a system. We have published some work on image-based 3-D tracking as well as pose estimation and human body model estimation from voxels. Since much of my work has revolved around multi-camera capture I have a fair bit of experience with different multiple capture systems. I have (with James Sherman) designed and built the Hydra project, a portable and scalable multi-camera capture system for human motion analysis. |
ProjectsI've listed below some of the projects that I have worked on as a graduate student at Maryland including a summer I spent at Honda Research Institute. Some of the information is duplicated in the NSF project page, but this is supposed to be the concise and up-to-date summary. |
Model driven human body model estimation in Laplacian Eigenspace |
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Articulated 3-D Tracking using shape and motion cues |
Real-time marker based motion capture to control robots
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Human Identification at a Distance (HID) |
PublicationsBook chapters
Journal articles
Conferences
Workshops and Symposiums
Presentations and posters
Courses and projectsCourses
Projects
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This project has two components. The first is model-driven segmentation
in Laplacian Eigenspace. The input abstraction layer is voxels and the
neighbourhood relationship of the voxels is used to compute the Laplacian
of the adjacency graph. The nodes are then mapped to 6-D Laplacian
eigenspace using the eigenvectors corresponding to the smallest non-zero
eigenvalues of the Laplacian matrix. We show that this transformation maps
segments whose lengths are greater than their thicknesses to 1-D curves in
eigenspace. We can then fit splines to these 1-D curves and segment them at
their joints. The two images on the left correspond to the 6-D eigenspace
and we have segmented one segment by fitting a spline. This work was
awarded the 
The second part of the project is to acquire a set of key frames where the
voxels have been segmented and registered using a prior model and a
probabilistic registration method. This set of key frames can be used to
estimate the human body model in two steps: estimate a skeleton based human
body model and joint locations using human body statistics and computed
skeleton, and then fit a super-quadric model using the segmented voxels.
The images (from left to right) denote the voxels (unsegmented), voxels
(segmented), computed skeleton curve and estimated super-quadric skeleton
model. Five frames were used to estimate the model.
We perform 3D tracking of articulated subjects using both motion and shape
cues information in the images obtained from multiple cameras. The motion
information used is the computed pixel displacement. The shape information
used is both the silhouette information as well as the "motion residue".
These cues are complementary and when fused in the tracking algorithm
prevent drifting (through use of shape features) and avoid non-optimal
local minima (through prediction of motion using optical flow). The two
images on the right illustrate the super-quadric model superimposed on the
image for two views.
The project was with Allen Yang (then at UIUC), James Davis, Hector
Gonzalez-Banos and Victor Ng-Thow-Hing at Honda Research Institute. The
objective is to retarget motion from a subject wearing markers to different
robots such as Asimo. Images were obtained from eight cameras attached to
two servers. The server can be controlled over network to capture,
calibrate, obtain 2D marker locations and 3D marker locations. The markers
are located in real time and their position in space found. The pose is
estimated from the markers and the motion is retargetted to the robot. The
motion retargetting was done by Allen and Hector.
The objective is to obtain methods of representing and recognizing
humans in video sequences. We use 2-D binary silhouettes in an HMM
framework for modelling gait and human shape. This simple approach enables
us to analyse gait and gain an understanding of the problem. We aim to
ultimately build 3-D models for human motion. Silhouettes are obtained from
the sequence and are used to build an exemplar image-based human shape and
gait model using the Baum-Welch algorithm. This HMM model can be used to
obtain the identity of an unknown subject by maximising the posterior
probability of the model given the sequence. The UMD data is available at
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Last updated Jul 23, 2008. |
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