DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations

• URL: http://arxiv.org/abs/2208.08190v1
• Date: Wed, 17 Aug 2022 09:55:02 GMT
• Title: DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations
• Authors: Gabriel Van Zandycke and Vladimir Somers and Maxime Istasse and Carlo Del Don and Davide Zambrano
• Abstract summary: This paper introduces DeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarks for automated sport understanding. The main purpose of this framework is to close the gap between academic research and real world settings.
• Score: 3.000319651350124
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues. This paper introduces DeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarks for automated sport understanding. The main purpose of this framework is to close the gap between academic research and real world settings. To this end, the datasets provide high-resolution raw images, camera parameters and high quality annotations. DeepSportradar currently supports four challenging tasks related to basketball: ball 3D localization, camera calibration, player instance segmentation and player re-identification. For each of the four tasks, a detailed description of the dataset, objective, performance metrics, and the proposed baseline method are provided. To encourage further research on advanced methods for sport understanding, a competition is organized as part of the MMSports workshop from the ACM Multimedia 2022 conference, where participants have to develop state-of-the-art methods to solve the above tasks. The four datasets, development kits and baselines are publicly available.

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)
• 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)
• SoccerNet 2023 Challenges Results [165.5977813812761]
  SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes.
  arXiv  Detail & Related papers  (2023-09-12T07:03:30Z)
• A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision [41.427845300209945]
  Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper focuses on three main aspects: algorithms, datasets and virtual environments, and challenges.
  arXiv  Detail & Related papers  (2023-07-07T02:22:17Z)
• Sports Video Analysis on Large-Scale Data [10.24207108909385]
  This paper investigates the modeling of automated machine description on sports video. We propose a novel large-scale NBA dataset for Sports Video Analysis (NSVA) with a focus on captioning.
  arXiv  Detail & Related papers  (2022-08-09T16:59:24Z)
• 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)
• A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions [3.138976077182707]
  This paper presents a review of sports video analysis for various applications high-level analysis. It includes detection and classification of players, tracking player or ball in sports, predicting the trajectories of player or ball, recognizing the teams strategies, classifying various events in sports.
  arXiv  Detail & Related papers  (2022-03-03T07:49:21Z)
• Distantly Supervised Semantic Text Detection and Recognition for Broadcast Sports Videos Understanding [0.0]
  We study extremely accurate semantic text detection and recognition in sports clocks. We propose a novel distant supervision technique to automatically build sports clock datasets. We share our computational architecture pipeline to scale this system in industrial setting.
  arXiv  Detail & Related papers  (2021-10-31T23:59:29Z)
• MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions [39.27858380391081]
  This paper aims to present a new multi-person dataset of atomic-temporal actions, coined as MultiSports. We build the dataset of MultiSports v1.0 by selecting 4 sports classes, collecting around 3200 video clips, and annotating around 37790 action instances with 907k bounding boxes.
  arXiv  Detail & Related papers  (2021-05-16T10:40:30Z)
• SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos [71.72665910128975]
  SoccerNet-v2 is a novel large-scale corpus of manual annotations for the SoccerNet video dataset. We release around 300k annotations within SoccerNet's 500 untrimmed broadcast soccer videos. We extend current tasks in the realm of soccer to include action spotting, camera shot segmentation with boundary detection.
  arXiv  Detail & Related papers  (2020-11-26T16:10:16Z)

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.