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AGTH-Net: Attention-Based Graph Convolution-Guided Third-Order Hourglass Network for Sports Video Classification.

Ming Gao¹, Weiwei Cai², Runmin Liu³

¹College of Sports Science and Technology of Wuhan Sports University, Wuhan 430205, China.

Journal of Healthcare Engineering

|July 26, 2021

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This study introduces an attention-based graph convolution-guided network (AGTH-Net) for automatic sports video classification. The novel model enhances accuracy by reducing manual influence and improving feature extraction for complex sports videos.

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Sports video classification is vital for applications like smart TV and video on demand.
Current manual classification methods are prone to human error and inaccuracy.
There is a need for automated, accurate sports video classification techniques.

Purpose of the Study:

To propose a novel neural network model for automatic sports video classification.
To address the limitations of manual classification by introducing an attention-based graph convolution approach.
To enhance the accuracy and efficiency of sports video analysis.

Main Methods:

Developed an attention-based graph convolution model for neighborhood node weight allocation, minimizing error node impact.

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Utilized a third-order hourglass network structure for multiscale feature extraction and fusion in complex sports videos.

Incorporated residual-intensive modules within the hourglass network for improved feature transfer and reuse.

Main Results:

The proposed AGTH-Net model demonstrated superior performance in sports video classification tasks.
Ablation experiments confirmed the effectiveness of the attention mechanism and hourglass structure.
The model successfully extracts and fuses multiscale features, enhancing classification accuracy.

Conclusions:

The AGTH-Net model offers a significant advancement in automatic sports video classification.
The integration of attention mechanisms and hourglass networks provides a robust solution for complex video data.
This research paves the way for more reliable and efficient sports video analysis systems.