Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Strike a Pose: Tracking People by Finding Stylized Poses

Deva Ramanan, David A. Forsyth and Andrew Zisserman

EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-04-1362
November 2004

http://www.eecs.berkeley.edu/Pubs/TechRpts/2004/CSD-04-1362.pdf

We develop an algorithm for finding and kinematically tracking multiple people in long sequences. Our basic assumption is that people tend to take on certain canonical poses, even when performing unusual activities like throwing a baseball or figure skating. We build a person detector that quite accurately detects and localizes limbs of people in lateral walking poses. We use the estimated limbs from a detection to build a discriminative appearance model; we assume the features that discriminate a figure in one frame will discriminate the figure in other frames. We then use the models as limb detectors in a pictorial structure framework, detecting figures in unrestricted poses in both previous and successive frames. We have run our tracker on hundreds of thousands of frames, and present and apply a methodology for evaluating tracking on such a large scale. We test our tracker on real sequences including a feature-length film, an hour of footage from a public park, and various sports sequences. We find that we can quite accurately automatically find and track multiple people interacting with each other while performing fast and unusual motions.


BibTeX citation:

@techreport{Ramanan:CSD-04-1362,
    Author = {Ramanan, Deva and Forsyth, David A. and Zisserman, Andrew},
    Title = {Strike a Pose: Tracking People by Finding Stylized Poses},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2004},
    Month = {Nov},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2004/5725.html},
    Number = {UCB/CSD-04-1362},
    Abstract = {We develop an algorithm for finding and kinematically tracking multiple people in long sequences. Our basic assumption is that people tend to take on certain canonical poses, even when performing unusual activities like throwing a baseball or figure skating. We build a person detector that quite accurately detects and localizes limbs of people in lateral walking poses. We use the estimated limbs from a detection to build a discriminative appearance model; we assume the features that discriminate a figure in one frame will discriminate the figure in other frames. We then use the models as limb detectors in a pictorial structure framework, detecting figures in unrestricted poses in both previous and successive frames. We have run our tracker on hundreds of thousands of frames, and present and apply a methodology for evaluating tracking on such a large scale. We test our tracker on real sequences including a feature-length film, an hour of footage from a public park, and various sports sequences. We find that we can quite accurately automatically find and track multiple people interacting with each other while performing fast and unusual motions.}
}

EndNote citation:

%0 Report
%A Ramanan, Deva
%A Forsyth, David A.
%A Zisserman, Andrew
%T Strike a Pose: Tracking People by Finding Stylized Poses
%I EECS Department, University of California, Berkeley
%D 2004
%@ UCB/CSD-04-1362
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2004/5725.html
%F Ramanan:CSD-04-1362