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.

HMDB-51 is an human motion recognition dataset with 51 activity classifications, which altogether contain around 7,000 physically clarified cuts separated from an assortment of sources going from digitized motion pictures to YouTube.It was developed by the researchers: H. Kuehne, H. Jhuang, E. Garrote and T.Serre in the year 2011. The dataset contains 51 particular activity classes, each containing at any rate 101 clips for an aggregate of 6,766 video cuts extricated from a wide scope of sources. The labels for each clip incorporate the camera viewpoint, the video quality, and the number of entertainers engaged with the activity. Here, we will examine data contained in this dataset, how it was gathered, and provide some benchmark models that gave high precision on this dataset. Further, we will implement the HMDB using Pytorch and Keras Library.