Meng Gao, Bingchun Ju

Attention-enhanced gated recurrent unit for action recognition in tennis

  • General Computer Science

Human Action Recognition (HAR) is an essential topic in computer vision and artificial intelligence, focused on the automatic identification and categorization of human actions or activities from video sequences or sensor data. The goal of HAR is to teach machines to comprehend and interpret human movements, gestures, and behaviors, allowing for a wide range of applications in areas such as surveillance, healthcare, sports analysis, and human-computer interaction. HAR systems utilize a variety of techniques, including deep learning, motion analysis, and feature extraction, to capture and analyze the spatiotemporal characteristics of human actions. These systems have the capacity to distinguish between various actions, whether they are simple actions like walking and waving or more complex activities such as playing a musical instrument or performing sports maneuvers. HAR continues to be an active area of research and development, with the potential to enhance numerous real-world applications by providing machines with the ability to understand and respond to human actions effectively. In our study, we developed a HAR system to recognize actions in tennis using an attention-based gated recurrent unit (GRU), a prevalent recurrent neural network. The combination of GRU architecture and attention mechanism showed a significant improvement in prediction power compared to two other deep learning models. Our models were trained on the THETIS dataset, one of the standard medium-sized datasets for fine-grained tennis actions. The effectiveness of the proposed model was confirmed by three different types of image encoders: InceptionV3, DenseNet, and EfficientNetB5. The models developed with InceptionV3, DenseNet, and EfficientNetB5 achieved average ROC-AUC values of 0.97, 0.98, and 0.81, respectively. While, the models obtained average PR-AUC values of 0.84, 0.87, and 0.49 for InceptionV3, DenseNet, and EfficientNetB5 features, respectively. The experimental results confirmed the applicability of our proposed method in recognizing action in tennis and may be applied to other HAR problems.

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