A New Perspective for Shuttlecock Hitting Event Detection
Yu-Hsi Chen
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.