8/10/2019 UD Stander Symposium 2014
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Regression based Time-Invariant Modelling for Human
Action/Gesture Recognition
Extract Features per frame:
Hierarchical Histogram of Flow (HHOF) :
Local distribution of local motion vectors.
Local Binary Flow Patterns( LBFP) :
Relationship between motion vectors.
R-Transform :
Gives shape representation of human pose.
Training Phase:
Accumulate all frames.
Extract Features.
Learn Eigen action basis
through PCA/GRNN.
Testing Phase:
Extract features
from few frames.
Find GRNN/Eigen
basis with lowest
discrepancy.
Proposed Methodology
Objectives
To develop a mathematical model to recognize human
actions/gestures
in real time (from 10-15 frames).
To find an appropriate actions basis on which no normalization
of action
cycle is required and is invariant to speed of motion.
Experiments
Weizmann Action Cambridge Hand Gesture
Learning Mechanism and Classification
Computation of Action Basis (Training)
Collection of all action features of class from training.
= : 1 ; :
Perform PCA on for action class to get Eigen action basis and
corresponding projections = {: 1 ;
}.
Learn mapping using generalized regression neural
network (GRNN).
Classification of action sequences (Testing)
Compute test projections , and estimate,
= (, ,)
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Binu M Nair
PhD Student
Electrical and Computer
Engineering
[email protected]
