Recognition of Human Gait
From Video
Rong Zhang, C. Vogler, and D. Metaxas
Computational Biomedicine Imaging and Modeling Center
Rutgers University
Outline
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Motivation
Distinguishing features
Recognition process
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Silhouette extraction
Human model initialization
Extracting joint angles over image sequences
Recognition
Preliminary Results
Motivation
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The goal is to detect and identify
humans by the way they walk.
The walking pattern (gait) is unique
enough to identify a person.
Such capabilities will enhance:
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Human identification.
Abnormal behavior detection.
Gait Cycle
Distinguishing features
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Features that seem unique to each
person:
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Joint angle between the upper and lower
legs
Relationship between the knee joints and
the feet over time
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Elevation of knee joint over the ankle (i.e.,
vertical distance between knee and ankle)
shows a distinctive temporal pattern
Elevation over ankle is distinctive
Transition from swing
leg to stance leg is noticeably
different across different people
over time
Gait Recognition Procedure
Image
sequences
Background
image
Silhouette
images
Human
model
Joint
angles
Recognition
Silhouette Extraction Result
Image
Background
After background subtraction
Final result
Human Model
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Human is modeled by five connected
trapezoids.
Each trapezoid (body part) is represented by
bpi  {r1i , r2i , li ,i }
r1i
li
r2i
i
Human Model
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Each configuration of human body is
represented by
H  {c, bp1, bp2 ,, bp5}
where bpi  {r1i , r2i , li ,i }, and c as the center
of the body.
Human Model Initialization –
Result
2D Model-based Human
tracking
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Previous methods
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Cardboard person model
Scaled Prismatic Model
Twist and exponential maps
Condensation-Based
Our approach
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Tracking via Gibbs sampling(probabilistic)
Advantages
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Able to handle occlusion implicitly
Has greater chance of avoiding local optima
Tracking Results
Tracking Results
Recognition
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Collect feature vector with:
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Elevation over ankle
Joint angles between upper and lower leg
Use left-right hidden Markov models for
recognition
One HMM per person, trained on a
minimum of 4-5 full step cycles from
that person
Recognition (continued)
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Use algorithm similar to isolated speech
recognition to identify people:
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Collect a step cycle from test subject
For each HMM in the database, compute
likelihood that it matches signal of this step
cycle
Select HMM with maximum likelihood
Person corresponding to that HMM is
identified subject
Experiment
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Two sequence sets are taken at two
locations: one in a parking lot, one in
front of a building
Each set contains 3 persons walking
sequences
Preliminary Results
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Perfect recognition scores across 3
subjects
5-6 step cycles per subject collected
from the computer vision algorithm
Use of HMMs with knee elevation and
joint angles as features holds promise
More work is needed to identify other
distinguishing features
Thank You!