human pose detection abhinav golas s. arun nair. overview problem previous solutions solution,...
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Human Pose detection
Abhinav Golas
S. Arun Nair
Overview
Problem Previous solutions Solution, details
Problem
Segmentation of humans from video capture
Pose detection (by fitting onto body model) Resistant to noise (background etc.)
Previous procedures
View problem as sequential process1. Segmentation2. Pose detection
Problems: Not using prior knowledge of “what a human looks
like” in segmentation Uses only information from detected “foreground”
for pose detection All available information not used
Solution
Combine segmentation and pose detection as a single step Uses all available information in frame (for pose
detection) Uses prior knowledge of human body for better
segmentation
PoseCut: Bray, Kohli, Torr Model segmentation as Bayesian labeling problem
with 2 labels: foreground, background
Details
Model problem as energy minimization problem – model as an MRF
Use a basic stickman model as a human body model
Adaptive model for background – GMM Neighbourhood terms – Generalised Potts
model
MRF – Markov Random Fields
Markov property for time:P(event:t) depends on events at times k<t
Markov property for space:P(event:x) depends on events at N(x) – neighbourhood of x
Use Gibbs energy model for solving We use neighbourhood of 8 pixels
Stickman model
Basic model 26 degrees of
freedom
GMM – Gaussian Mixture Model
Model each pixel of image as a weighted sum of Gaussian functions
Adapt functions using each new frame Pixel matches expected value –
background, else foreground
Execution details
For each frame Calculate weights for GMM, Potts model For given value of 26 vector (based on degrees of
freedom of stickman model) calculate energy cost for stickman model (by distance transform)
Minimize energy for Bayesian labeling by graph cut Minimize 26 vector by repeated graph cuts by
Powell's algorithm
Sample results
A – original frame B – segmentation by
colour likelihood and contrast terms
C – when GMM terms are taken
D – with pose prior components
E – deduced pose
Comparisons