Sport Task: Fine Grained Action Detection and Classification of Table
Tennis Strokes from Videos for MediaEval 2022
- URL: http://arxiv.org/abs/2301.13576v1
- Date: Tue, 31 Jan 2023 12:03:59 GMT
- Title: Sport Task: Fine Grained Action Detection and Classification of Table
Tennis Strokes from Videos for MediaEval 2022
- Authors: Pierre-Etienne Martin (MPI-EVA), Jordan Calandre (MIA), Boris
Mansencal (LaBRI), Jenny Benois-Pineau (LaBRI), Renaud P\'eteri (MIA),
Laurent Mascarilla (MIA), Julien Morlier
- Abstract summary: This task aims at detecting and classifying subtle movements from sport videos.
We focus on recordings of table tennis matches.
Since 2021, the task also provides a stroke detection challenge from unannotated, untrimmed videos.
- Score: 0.9894420655516565
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Sports video analysis is a widespread research topic. Its applications are
very diverse, like events detection during a match, video summary, or
fine-grained movement analysis of athletes. As part of the MediaEval 2022
benchmarking initiative, this task aims at detecting and classifying subtle
movements from sport videos. We focus on recordings of table tennis matches.
Conducted since 2019, this task provides a classification challenge from
untrimmed videos recorded under natural conditions with known temporal
boundaries for each stroke. Since 2021, the task also provides a stroke
detection challenge from unannotated, untrimmed videos. This year, the
training, validation, and test sets are enhanced to ensure that all strokes are
represented in each dataset. The dataset is now similar to the one used in [1,
2]. This research is intended to build tools for coaches and athletes who want
to further evaluate their sport performances.
Related papers
- Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex
and Professional Sports [90.79212954022218]
We introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task.
Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions.
We propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering.
arXiv Detail & Related papers (2024-01-03T02:22:34Z) - Towards Active Learning for Action Spotting in Association Football
Videos [59.84375958757395]
Analyzing football videos is challenging and requires identifying subtle and diverse-temporal patterns.
Current algorithms face significant challenges when learning from limited annotated data.
We propose an active learning framework that selects the most informative video samples to be annotated next.
arXiv Detail & Related papers (2023-04-09T11:50:41Z) - P2ANet: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos [64.57435509822416]
This work consists of 2,721 video clips collected from the broadcasting videos of professional table tennis matches in World Table Tennis Championships and Olympiads.
We formulate two sets of action detection problems -- emphaction localization and emphaction recognition.
The results confirm that TheName is still a challenging task and can be used as a special benchmark for dense action detection from videos.
arXiv Detail & Related papers (2022-07-26T08:34:17Z) - A Survey on Video Action Recognition in Sports: Datasets, Methods and
Applications [60.3327085463545]
We present a survey on video action recognition for sports analytics.
We introduce more than ten types of sports, including team sports, such as football, basketball, volleyball, hockey and individual sports, such as figure skating, gymnastics, table tennis, diving and badminton.
We develop a toolbox using PaddlePaddle, which supports football, basketball, table tennis and figure skating action recognition.
arXiv Detail & Related papers (2022-06-02T13:19:36Z) - SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in
Soccer Videos [62.686484228479095]
We propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each.
The dataset is fully annotated with bounding boxes and tracklet IDs.
Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved.
arXiv Detail & Related papers (2022-04-14T12:22:12Z) - Sports Video: Fine-Grained Action Detection and Classification of Table
Tennis Strokes from Videos for MediaEval 2021 [0.0]
This task tackles fine-grained action detection and classification from videos.
The focus is on recordings of table tennis games.
This work aims at creating tools for sports coaches and players in order to analyze sports performance.
arXiv Detail & Related papers (2021-12-16T10:17:59Z) - Hybrid Dynamic-static Context-aware Attention Network for Action
Assessment in Long Videos [96.45804577283563]
We present a novel hybrid dynAmic-static Context-aware attenTION NETwork (ACTION-NET) for action assessment in long videos.
We learn the video dynamic information but also focus on the static postures of the detected athletes in specific frames.
We combine the features of the two streams to regress the final video score, supervised by ground-truth scores given by experts.
arXiv Detail & Related papers (2020-08-13T15:51:42Z) - Event detection in coarsely annotated sports videos via parallel multi
receptive field 1D convolutions [14.30009544149561]
In problems such as sports video analytics, it is difficult to obtain accurate frame level annotations and exact event duration.
We propose the task of event detection in coarsely annotated videos.
We introduce a multi-tower temporal convolutional network architecture for the proposed task.
arXiv Detail & Related papers (2020-04-13T19:51:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.