A New Perspective for Shuttlecock Hitting Event Detection
This work addresses a domain-specific problem for badminton analysis, presenting a novel method but likely incremental as it adapts existing deep learning techniques to a niche task.
The paper tackles shuttlecock hitting event detection in badminton videos by using a deep learning model called SwingNet to analyze image sequences, aiming to accurately recognize hitting events with reduced learning difficulty through prior feature extraction.
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.