BlazePose-Based Action Recognition with Feature Selection Using Stochastic Fractal Search Guided Whale Optimization

The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. A Spatial-Temporal Graph Convolutional Network can then be used to forecast the actions. This architecture performance can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph's topology and then fix it across all input layers and samples. This research shows how to use Stochastic Fractal Search - Guided Whale Optimization Algorithm in conjunction with the BlazePose skeletal data to construct a novel implementation of this topology for action recognition. We utilized the NTU-RGB+D and the Kinetics datasets as benchmarks in our experiments.