FACTS: Fine-Grained Action Classification for Tactical Sports

• URL: http://arxiv.org/abs/2412.16454v1
• Date: Sat, 21 Dec 2024 03:00:25 GMT
• Title: FACTS: Fine-Grained Action Classification for Tactical Sports
• Authors: Christopher Lai, Jason Mo, Haotian Xia, Yuan-fang Wang,
• Abstract summary: Classifying fine-grained actions in fast-paced, close-combat sports such as fencing and boxing presents unique challenges. We introduce FACTS, a novel approach for fine-grained action recognition that processes raw video data directly. Our findings enhance training, performance analysis, and spectator engagement, setting a new benchmark for action classification in tactical sports.
• Score: 4.810476621219244
• License:
• Abstract: Classifying fine-grained actions in fast-paced, close-combat sports such as fencing and boxing presents unique challenges due to the complexity, speed, and nuance of movements. Traditional methods reliant on pose estimation or fancy sensor data often struggle to capture these dynamics accurately. We introduce FACTS, a novel transformer-based approach for fine-grained action recognition that processes raw video data directly, eliminating the need for pose estimation and the use of cumbersome body markers and sensors. FACTS achieves state-of-the-art performance, with 90% accuracy on fencing actions and 83.25% on boxing actions. Additionally, we present a new publicly available dataset featuring 8 detailed fencing actions, addressing critical gaps in sports analytics resources. Our findings enhance training, performance analysis, and spectator engagement, setting a new benchmark for action classification in tactical sports.

Related papers

• Deep learning for action spotting in association football videos [64.10841325879996]
  The SoccerNet initiative organizes yearly challenges, during which participants from all around the world compete to achieve state-of-the-art performances. This paper traces the history of action spotting in sports, from the creation of the task back in 2018, to the role it plays today in research and the sports industry.
  arXiv  Detail & Related papers  (2024-10-02T07:56:15Z)
• FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition [57.17966905865054]
  Real-life applications of action recognition often require a fine-grained understanding of subtle movements. Existing semi-supervised action recognition has mainly focused on coarse-grained action recognition. We propose an Alignability-Verification-based Metric learning technique to effectively discriminate between fine-grained action pairs.
  arXiv  Detail & Related papers  (2024-09-02T20:08:06Z)
• Benchmarking Badminton Action Recognition with a New Fine-Grained Dataset [16.407837909069073]
  We introduce the VideoBadminton dataset derived from high-quality badminton footage. The introduction of VideoBadminton could not only serve for badminton action recognition but also provide a dataset for recognizing fine-grained actions.
  arXiv  Detail & Related papers  (2024-03-19T02:52:06Z)
• Automated Hit-frame Detection for Badminton Match Analysis [1.3300217947936062]
  This research aims to advance sports analysis in badminton, systematically detecting hit-frames automatically from match videos using modern deep learning techniques. The data included in hit-frames can subsequently be utilized to synthesize players' strokes and on-court movement, as well as for other downstream applications such as analyzing training tasks and competition strategy. In the study, we achieved 99% accuracy on shot angle recognition for video trimming, over 92% accuracy for applying player keypoints sequences on shuttlecock flying direction prediction, and reported the evaluation results of rally-wise video trimming and hit-frame detection.
  arXiv  Detail & Related papers  (2023-07-29T15:01:27Z)
• 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)
• 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)
• Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition [17.84533144792773]
  Video Pose Distillation (VPD) is a weakly-supervised technique to learn features for new video domains. VPD features improve performance on few-shot, fine-grained action recognition, retrieval, and detection tasks in four real-world sports video datasets.
  arXiv  Detail & Related papers  (2021-09-03T04:36:12Z)
• 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)
• Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos [87.29451470527353]
  We propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos. An algorithm is proposed to estimate the global motions from the mixed motions based on the intrinsic property of camera adjustments. A two-stream 3D CNN framework is utilized for group activity recognition over the separated global and local motion patterns.
  arXiv  Detail & Related papers  (2020-07-13T10:15:44Z)
• FSD-10: A Dataset for Competitive Sports Content Analysis [29.62110021022271]
  Figure Skating dataset (FSD-10) is designed to have a large collection of finegrained actions. Each clip is at a rate of 30 frames per second with resolution 1080 $times$ 720. We evaluate state-of-the-art action recognition methods on FSD-10.
  arXiv  Detail & Related papers  (2020-02-09T08:04:26Z)

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.