Research on Footprint Image Recognition Method for Aerobics Athletes Based on Multi-Feature Fusion

Footprint image recognition for athletes in aerobics training contains the postural and movement information richly and is of great value for motion analysis and performance assessment, once automatic identification is successfully performed. The proposed topic is 'footprints' image recognition set in the context of multi-feature fusion, which encompasses geometric, texture and deep features in that manner, the built method turned to be more precise and reliable finding the resolve. First of all, the fudged footprint pictures are matched and the features are extracted utilizing a mix of traditional techniques and deep learning strategies by which various feature representations are acquired. In consequence, through the merging of different modalities at both the feature and decision levels, a flexible recognition model is established. Lastly, public datasets' experiments come out with the fact that this approach beats the existing methods significantly. The outcomes of new research show that the proposed method performs better than the traditional single-feature recognition methods in the areas of accuracy, recall, and other evaluation parameters. The research findings provide effective technical support for aerobics movement analysis and footprint image processing in other fields.