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There has been a dramatic increase in the volume of videos and their relatedcontent uploaded to the internet. Accordingly, the need for efficientalgorithms to analyse this vast amount of data has attracted significantresearch interest. An action recognition system based upon human body motionshas been proven to interpret videos contents accurately. This work aims torecognize activities of daily living using the ST-GCN model, providing acomparison between four different partitioning strategies: spatialconfiguration partitioning, full distance split, connection split, and indexsplit. To achieve this aim, we present the first implementation of the ST-GCNframework upon the HMDB-51 dataset. We have achieved 48.88 % top-1 accuracy byusing the connection split partitioning approach. Through experimentalsimulation, we show that our proposals have achieved the highest accuracyperformance on the UCF-101 dataset using the ST-GCN framework than thestate-of-the-art approach. Finally, accuracy of 73.25 % top-1 is achieved byusing the index split partitioning strategy.

Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recogntion