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Found 10 papers, 7 papers with code
Dataset Generation and Bonobo Classification from Weakly Labelled Videos
Performance is compared in terms of classification accuracy on the splits of the database using different data separation methods.
Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks
As participants of the MediaEval 2022 Sport Task, we propose a two-stream network approach for the classification and detection of table tennis strokes.
Ranked #1 on
Action Classification
on TTStroke-21 ME22
Baseline Method for the Sport Task of MediaEval 2022 with 3D CNNs using Attention Mechanisms
We propose two types of 3D-CNN architectures to solve the two subtasks.
Ranked #1 on
Action Detection
on TTStroke-21 ME22
Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022
Since 2021, the task also provides a stroke detection challenge from unannotated, untrimmed videos.
3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition
3D convolutional networks is a good means to perform tasks such as video segmentation into coherent spatio-temporal chunks and classification of them with regard to a target taxonomy.
Spatio-Temporal CNN baseline method for the Sports Video Task of MediaEval 2021 benchmark
This task proposes a stroke detection and a stroke classification subtasks.
Ranked #1 on
Action Detection
on TTStroke-21 ME21
Two Stream Network for Stroke Detection in Table Tennis
This paper presents a table tennis stroke detection method from videos.
Ranked #2 on
Action Detection
on TTStroke-21 ME21
Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021
Sports video analysis is a prevalent research topic due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests up to analysis of athletes' performance.
Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table Tennis
The goal is to detect and classify table tennis strokes in the videos, the first step of a bigger scheme aiming at giving feedback to the players for improving their performance.
3D attention mechanism for fine-grained classification of table tennis strokes using a Twin Spatio-Temporal Convolutional Neural Networks
In the context of the study of sportsmen performances, a corpus of the particular actions of table tennis strokes is considered.
