Aravind Sundaresan and Rama Chellappa
Download
1 / 10

Multi-camera Tracking of Articulated Human Motion using Motion and Shape Cues - PowerPoint PPT Presentation


  • 90 Views
  • Uploaded on

Aravind Sundaresan and Rama Chellappa Center for Automation Research University of Maryland, College Park MD USA. Multi-camera Tracking of Articulated Human Motion using Motion and Shape Cues. What is motion capture?.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Multi-camera Tracking of Articulated Human Motion using Motion and Shape Cues' - simeon


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Aravind Sundaresan and Rama Chellappa

Center for Automation Research

University of Maryland, College Park MD USA

Multi-camera Tracking of Articulated Human Motion using Motion and Shape Cues


What is motion capture
What is motion capture?

Motion capture (Mocap) is the process of analysing and expressing human motion in mathematical terms.

Initialisation, Pose estimation and Tracking.

Applications

Motion Analysis for clinical studies, Human-computer interaction, Computer animation.

Marker-based systems have shortcomings

Cumbersome, introduce artefacts, time consuming.

Marker-less system desirable.


Calibration and human body model
Calibration and Human body model

Use multiple cameras (8) in our capture

640x480 grey scale images at 30 fps.

Calibrated using algorithm of Svoboda.

Use articulated human body model.

Super-quadrics for body segments.

Model described by joint locations and super-quadrics.

Pose is described by joint angles.


Overview
Overview

Use images from multiple cameras.

Compute 2-D pixel displacement between t and t+1.

Predict 3-D pose at t+1 using pixel displacement.

Compute spatial energy function as function of pose.

Minimise energy function to obtain pose at t+1.


Tracking framework
Tracking Framework

Use motion and spatial cues for tracking.

Motion cues use texture.

Error accumulation: estimates only change in pose.

Spatial cues obtained from silhouettes, edges, etc.

Instability: Solutions are stable only “locally”.

Predictor-Corrector framework.

Predictor:

Compute motion(t) from pixel displacement.

Predict pose(t+1) from pose(t) and motion(t).

Corrector:

Assimilate spatial cues into single energy function.

Correct pose(t+1) by minimising energy function.


Pixel registration and displacement
Pixel registration and displacement

Project model onto image to obtain

Body part label for pixel.

3-D location of pixel.

Mask for each body part

Find dense pixel correspondence using

Parametric optical flow-based algorithm for each segment..

Minimise MSE:


Pose from pixel displacement
Pose from pixel displacement

State-space formulation

  • Linearisation

    • We show that

    • Taylor series

    • Iteratively estimate pose


Combine spatial cues
Combine spatial cues

Combine multiple spatial cues into a single “spatial energy function”.

Compute pose energy as function of dx, dy and Φ.

+

=


Minimise 3d pose energy
Minimise 3D pose energy

Given multiple views and 3-D pose

Compute 2-D pose for ith image

Compute Ei for ith camera using 2-D pose

3D pose energy, E = E1+ E2 + ... + En

Compute minimum energy pose using optimisation.



ad