Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recogntion. (arXiv:2111.03106v1 [cs.CV])

There has been a dramatic increase in the volume of videos and their related
content uploaded to the internet. Accordingly, the need for efficient
algorithms to analyse this vast amount of data has attracted significant
research interest. An action recognition system based upon human body motions
has been proven to interpret videos contents accurately. This work aims to
recognize activities of daily living using the ST-GCN model, providing a
comparison between four different partitioning strategies: spatial
configuration partitioning, full distance split, connection split, and index
split. To achieve this aim, we present the first implementation of the ST-GCN
framework upon the HMDB-51 dataset. We have achieved 48.88 % top-1 accuracy by
using the connection split partitioning approach. Through experimental
simulation, we show that our proposals have achieved the highest accuracy
performance on the UCF-101 dataset using the ST-GCN framework than the
state-of-the-art approach. Finally, accuracy of 73.25 % top-1 is achieved by
using the index split partitioning strategy.



Related post