A New Perspective for Shuttlecock Hitting Event Detection

TL;DR

This paper presents a deep learning-based method using SwingNet and video processing techniques to accurately detect shuttlecock hitting events in badminton videos, offering a new perspective on event detection.

Contribution

It introduces a novel approach that combines sequence reasoning and specialized video features with SwingNet for improved badminton hitting event detection.

Findings

Effective detection of hitting events achieved

Reduced learning difficulty through feature extraction

Provides an intuitive, user-friendly detection method

Abstract

This article introduces a novel approach to shuttlecock hitting event detection. Instead of depending on generic methods, we capture the hitting action of players by reasoning over a sequence of images. To learn the features of hitting events in a video clip, we specifically utilize a deep learning model known as SwingNet. This model is designed to capture the relevant characteristics and patterns associated with the act of hitting in badminton. By training SwingNet on the provided video clips, we aim to enable the model to accurately recognize and identify the instances of hitting events based on their distinctive features. Furthermore, we apply the specific video processing technique to extract the prior features from the video, which significantly reduces the learning difficulty for the model. The proposed method not only provides an intuitive and user-friendly approach but also presents a fresh perspective on the task of detecting badminton hitting events. The source code will be available at https://github.com/TW-yuhsi/A-New-Perspective-for-Shuttlecock-Hitting-Event-Detection.