Real-time human pose tracking from range data
Abstract/Contents
- Abstract
- Real-time human pose tracking enables a variety of applications, including intuitive human-machine interaction, smart surveillance, character animation, virtual reality, gaming, and physical therapy. Traditional approaches have required multiple cameras and special suits with markers, rendering these techniques impractical for most consumer applications. Consequently, recent research has focused on marker-less human pose estimation using only a single camera. We approach this problem with a single consumer-grade range camera, which is an active sensor that measures distance at each pixel. While direct distance measurements greatly assist the reconstruction problem, these cameras are low resolution and noisy compared to traditional color cameras, and self-occlusion causes ambiguities when attempting to reconstruct pose from a single view. This thesis presents a new real-time human pose tracking algorithm based on a generative model of the range camera measurement process. Bayes' theorem is applied to find the maximum a-posteriori estimate of the pose given the measured range image and priors on human shape. The resulting non-convex optimization problem is difficult to solve, often containing many plateaus and multiple local maxima. We address these difficulties with an algorithm comprised of an outer loop that proposes new poses based on a prior on motion and part detections from the observed image, and an inner loop that refines the pose to better match the observations. The latter refinement itself decomposes into two alternating phases; the first establishes correspondences between observations and the surface of the human model, while the second updates the pose given the correspondences via a constrained continuous optimization. For quantitative evaluation, a large dataset was collected using two types of range cameras and a traditional marker-based motion capture system. The presented algorithm is able to accurately track complicated full-body movements involving significant self-occlusion and fast motions on humans of different sizes and shapes without any explicit initialization. The algorithm runs at more than 30 frames per second on a consumer computer using about half of one processor core.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2014 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Ganapathi, Hariraam Varun |
|---|---|
| Associated with | Stanford University, Department of Computer Science. |
| Primary advisor | Koller, Daphne |
| Thesis advisor | Koller, Daphne |
| Thesis advisor | Ng, Andrew Hock-soon, 1972- |
| Thesis advisor | Thrun, Sebastian, 1967- |
| Advisor | Ng, Andrew Hock-soon, 1972- |
| Advisor | Thrun, Sebastian, 1967- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Hariraam Varun Ganapathi. |
|---|---|
| Note | Submitted to the Department of Computer Science. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2014. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Varun Ganapathi
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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