DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes

• URL: http://arxiv.org/abs/2112.00627v1
• Date: Wed, 1 Dec 2021 16:30:51 GMT
• Title: DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes
• Abstract summary: This paper presents a unified framework to (i) locate the ball, (ii) predict the pose, and (iii) segment the instance mask of players in team sports scenes.
• Score: 19.845244830593067
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents a unified framework to (i) locate the ball, (ii) predict the pose, and (iii) segment the instance mask of players in team sports scenes. Those problems are of high interest in automated sports analytics, production, and broadcast. A common practice is to individually solve each problem by exploiting universal state-of-the-art models, \eg, Panoptic-DeepLab for player segmentation. In addition to the increased complexity resulting from the multiplication of single-task models, the use of the off-the-shelf models also impedes the performance due to the complexity and specificity of the team sports scenes, such as strong occlusion and motion blur. To circumvent those limitations, our paper proposes to train a single model that simultaneously predicts the ball and the player mask and pose by combining the part intensity fields and the spatial embeddings principles. Part intensity fields provide the ball and player location, as well as player joints location. Spatial embeddings are then exploited to associate player instance pixels to their respective player center, but also to group player joints into skeletons. We demonstrate the effectiveness of the proposed model on the DeepSport basketball dataset, achieving comparable performance to the SoA models addressing each individual task separately.

Related papers

• BasketEvent: Understanding Who Did What and When in Basketball Videos [51.926771112716686]
  We introduce BasketEvent, a player- centric basketball event understanding dataset curated from real NBA broadcasts.<n>We propose PlayNet, a player-centric reasoning framework that maps basketball videos to player-level event predictions.<n>Our data, code, and models will be made publicly available.
  arXiv  Detail & Related papers  (2026-07-23T12:39:56Z)
• Mesh-Aware Epipolar Matching for Multi-View Multi-Person 3D Pose Estimation in Basketball [10.727074525703147]
  Mesh-Aware Epipolar Matching (MAEM) is a training-free framework for multi-view multi-person 3D pose estimation.<n> Experiments on two public multi-view basketball datasets demonstrate that MAEM consistently outperforms existing training-free association baselines.
  arXiv  Detail & Related papers  (2026-05-28T13:59:30Z)
• ActionParty: Multi-Subject Action Binding in Generative Video Games [117.52562594944679]
  ActionParty is a controllable multi-subject world model for generative video games.<n>We demonstrate the first video world model capable of controlling up to seven players simultaneously across 46 diverse environments.
  arXiv  Detail & Related papers  (2026-04-02T17:59:58Z)
• Multi-Modal Soccer Scene Analysis with Masked Pre-Training [16.853768247588743]
  We propose a multi-modal architecture for analyzing soccer scenes from tactical camera footage.<n>Our solution integrates three distinct input modalities into a unified framework.<n>We show the effectiveness of our approach on a large-scale dataset.
  arXiv  Detail & Related papers  (2025-12-22T16:18:45Z)
• SoccerMaster: A Vision Foundation Model for Soccer Understanding [50.88251190999469]
  Soccer understanding has recently garnered growing research interest due to its domain-specific complexity and unique challenges.<n>This work aims to propose a unified model to handle diverse soccer visual understanding tasks, ranging from fine-grained perception to semantic reasoning.<n>We present SoccerMaster, the first soccer-specific vision foundation model that unifies diverse understanding tasks within a single framework.
  arXiv  Detail & Related papers  (2025-12-11T18:03:30Z)
• KASportsFormer: Kinematic Anatomy Enhanced Transformer for 3D Human Pose Estimation on Short Sports Scene Video [4.653030985708889]
  We introduce KASportsFormer, a novel transformer based 3D pose estimation framework for sports.<n>Our proposed method achieves state-of-the-art results with MPJPE errors of 58.0mm and 34.3mm, respectively.
  arXiv  Detail & Related papers  (2025-07-28T12:17:40Z)
• You Only Estimate Once: Unified, One-stage, Real-Time Category-level Articulated Object 6D Pose Estimation for Robotic Grasping [119.41166438439313]
  YOEO is a single-stage method that outputs instance segmentation and NPCS representations in an end-to-end manner.<n>We use a unified network to generate point-wise semantic labels and centroid offsets, allowing points from the same part instance to vote for the same centroid.<n>We also deploy our synthetically-trained model in a real-world setting, providing real-time visual feedback at 200Hz.
  arXiv  Detail & Related papers  (2025-06-06T03:49:20Z)
• Object-level Scene Deocclusion [92.39886029550286]
  We present a new self-supervised PArallel visible-to-COmplete diffusion framework, named PACO, for object-level scene deocclusion. To train PACO, we create a large-scale dataset with 500k samples to enable self-supervised learning. Experiments on COCOA and various real-world scenes demonstrate the superior capability of PACO for scene deocclusion, surpassing the state of the arts by a large margin.
  arXiv  Detail & Related papers  (2024-06-11T20:34:10Z)
• Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports [53.637837706712794]
  We propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs. Specifically, we introduce a Ghost Spatial Masking (GSM) module, embedded within a Transformer encoder, for spatial feature extraction. We benchmark three practical sports datasets, Basketball-U, Football-U, and Soccer-U, for evaluation.
  arXiv  Detail & Related papers  (2024-05-27T22:15:23Z)
• Unifying Global and Local Scene Entities Modelling for Precise Action Spotting [5.474440128682843]
  We introduce a novel approach that analyzes and models scene entities using an adaptive attention mechanism. Our model has demonstrated outstanding performance, securing the 1st place in the SoccerNet-v2 Action Spotting, FineDiving, and FineGym challenge.
  arXiv  Detail & Related papers  (2024-04-15T17:24:57Z)
• Domain-Guided Masked Autoencoders for Unique Player Identification [62.87054782745536]
  Masked autoencoders (MAEs) have emerged as a superior alternative to conventional feature extractors. Motivated by human vision, we devise a novel domain-guided masking policy for MAEs termed d-MAE. We conduct experiments on three large-scale sports datasets.
  arXiv  Detail & Related papers  (2024-03-17T20:14:57Z)
• Estimation of control area in badminton doubles with pose information from top and back view drone videos [11.679451300997016]
  We present the first annotated drone dataset from top and back views in badminton doubles. We propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance.
  arXiv  Detail & Related papers  (2023-05-07T11:18:39Z)
• A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification [75.93186954061943]
  Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences. In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs. For the player identification task, our method obtains an overall performance of 57.83% average-mAP by combining it with other modalities.
  arXiv  Detail & Related papers  (2022-11-22T15:23:53Z)
• 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)
• Graph Neural Networks to Predict Sports Outcomes [0.0]
  We introduce a sport-agnostic graph-based representation of game states. We then use our proposed graph representation as input to graph neural networks to predict sports outcomes.
  arXiv  Detail & Related papers  (2022-07-28T14:45:02Z)
• 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)
• Unsupervised Object Learning via Common Fate [61.14802390241075]
  Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling. We decompose this problem into three easier subtasks, and provide candidate solutions for each of them. We show that our approach allows learning generative models that generalize beyond the occlusions present in the input videos.
  arXiv  Detail & Related papers  (2021-10-13T08:22:04Z)
• Predicting the outcome of team movements -- Player time series analysis using fuzzy and deep methods for representation learning [0.0]
  We provide a framework for the useful encoding of short tactics and space occupations in a more extended sequence of movements or tactical plans. We discuss the effectiveness of the proposed approach for prediction and recognition tasks on the professional basketball SportVU dataset for the 2015-16 half-season.
  arXiv  Detail & Related papers  (2021-09-13T18:42:37Z)
• Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts [86.56462654572813]
  We focus our analysis on action spotting in soccer broadcast, which consists in temporally localizing the main actions in a soccer game. We propose a novel feature pooling method based on NetVLAD, dubbed NetVLAD++, that embeds temporally-aware knowledge. We train and evaluate our methodology on the recent large-scale dataset SoccerNet-v2, reaching 53.4% Average-mAP for action spotting.
  arXiv  Detail & Related papers  (2021-04-14T11:09:03Z)

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.