Women Sport Actions Dataset for Visual Classification Using Small Scale Training Data

TL;DR

This paper introduces the WomenSports dataset for women sports action classification with small training data and proposes a CNN with channel attention for improved feature extraction, achieving high accuracy across multiple datasets.

Contribution

The work provides a new diverse dataset for women sports actions and develops a CNN with channel attention to enhance classification performance with limited data.

Findings

Achieved 89.15% accuracy on WomenSports dataset

Demonstrated effectiveness across multiple sports and dance datasets

Proposed CNN with channel attention improves feature representation

Abstract

Sports action classification representing complex body postures and player-object interactions is an emerging area in image-based sports analysis. Some works have contributed to automated sports action recognition using machine learning techniques over the past decades. However, sufficient image datasets representing women sports actions with enough intra- and inter-class variations are not available to the researchers. To overcome this limitation, this work presents a new dataset named WomenSports for women sports classification using small-scale training data. This dataset includes a variety of sports activities, covering wide variations in movements, environments, and interactions among players. In addition, this study proposes a convolutional neural network (CNN) for deep feature extraction. A channel attention scheme upon local contextual regions is applied to refine and enhance feature representation. The experiments are carried out on three different sports datasets and one dance dataset for generalizing the proposed algorithm, and the performances on these datasets are noteworthy. The deep learning method achieves 89.15% top-1 classification accuracy using ResNet-50 on the proposed WomenSports dataset, which is publicly available for research at Mendeley Data.