Child action recognition with GNN
objectives
first detailed ST-GNN based implementations on child action recognition
we have done detailed experiments on how intra-class and inter-class variations in child actions affect the model performance
we have utilized transfer learning methods to improve the performance on child action recognition
we develop a experimental method to measure the relationship between model accuracy and pose estimation noise and show that improved pose estimation result in improved model performance
experiment rationales
CWBG full: this is the main protocol/method for "child action recognition"
CWBG-dissimilar and CWBG-similar: these 2 for studying how inter-class variation in CAR affect the model performace
CWBG shared: this is the 5 shared classes. this is used to compare how train adult/test child VS train child/test child VS TFL with adult/test child VS large-diverse source dataset train/child test works basically compares how much improvement TFL can result in
intra-class variations
age wise - 3/4/5
gender wise male/female
Xsub vs random
check how if xsub is below random
this is what we expect since random has some person-related information shared -we expect random to perform better so
LOOCV
used to see how model works on each individual
can sort who is the best and worst; may give some insights
LOOCV is a better representation of the model also
ST-GNN
we are trying to see if improved/SOTA models can result in improvement :if so that means there is still potential for improved models to perform better
also results alone can be considered as a comparison
TFL methods
FX
FT
Hybrid
propagation
all these methods are used to see how far we can improve the model performance with source datasets
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ST-GCN architecture tuning -
this was done to see how much improvement can be achieved by changing the capacity of the model in depth and width wise
Research questions
Charith sir's feedback
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only within the given age group
between groups ex:3,4 vs 5
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GNN title justification: need experimental results to show that other skeleton methods perform worse than GNN
need to compare st-gcn as it is vs improved stgcn with child data
can use to analyse how age and gender groups perform on average
have to show evidence for all the Research questions
dharshana sir's feedback
need HAR(adult) vs child comparison
may be use NTU-5 for that
pradeepa madam feedback
include novelty
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