Spatio-Temporal CNN Baseline Method for the Sports Video Task of MediaEval 2021 Benchmark
Published in
MediaEval Benchmarking Initiative for Multimedi...(2021)External Links:
TL
TL;DR
The baseline method proposed for the Sports Video task part of the MediaEval 2021 benchmark proposes a stroke detection and a stroke classification subtasks and addresses both subtasks.
Abstract
This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2021 benchmark. This task proposes a stroke detection and a stroke classification subtasks. This baseline addresses both subtasks. The spatio-temporal CNN architecture and the training process of the model are tailored according to the addressed subtask. The method has the purpose of helping the participants to solve the task and is not meant to reach stateof-the-art performance. Still, for the detection task, the baseline is performing better than the other participants, which stresses the difficulty of such a task.
References21 items
1
Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from videos for MediaEval 2021
2
Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table Tennis
3
Siamese Spatio-Temporal Convolutional Neural Network for Stroke Classification in Table Tennis Games
4
MARS: Motion-Augmented RGB Stream for Action Recognition
5