Knock, knock. Who's there? -- Identifying football player jersey numbers with synthetic data

• URL: http://arxiv.org/abs/2203.00734v1
• Date: Tue, 1 Mar 2022 20:44:34 GMT
• Title: Knock, knock. Who's there? -- Identifying football player jersey numbers with synthetic data
• Authors: Divya Bhargavi, Erika Pelaez Coyotl, Sia Gholami
• Abstract summary: We present a novel approach for jersey number identification in a small, highly imbalanced dataset from the Seattle Seahawks practice videos. Our results indicate that simple models can achieve an acceptable performance on the jersey number detection task and that synthetic data can improve the performance dramatically.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automatic player identification is an essential and complex task in sports video analysis. Different strategies have been devised over the years, but identification based on jersey numbers is one of the most common approaches given its versatility and relative simplicity. However, automatic detection of jersey numbers is still challenging due to changing camera angles, low video resolution, small object size in wide-range shots and transient changes in the player's posture and movement. In this paper we present a novel approach for jersey number identification in a small, highly imbalanced dataset from the Seattle Seahawks practice videos. Our results indicate that simple models can achieve an acceptable performance on the jersey number detection task and that synthetic data can improve the performance dramatically (accuracy increase of ~9% overall, ~18% on low frequency numbers) making our approach achieve state of the art results.

Related papers

• Generalized Jersey Number Recognition Using Multi-task Learning With Orientation-guided Weight Refinement [12.058303459124003]
  Jersey number recognition (JNR) has always been an important task in sports analytics. Recent research has addressed these problems using number localization and optical character recognition. This paper proposes a multi-task learning method called the angle-digit scheme (ADRS), which combines human body orientation angles and digit number clues to recognize athletic jersey numbers.
  arXiv  Detail & Related papers  (2024-06-03T06:35:11Z)
• A General Framework for Jersey Number Recognition in Sports Video [5.985204759362746]
  Jersey number recognition is an important task in sports video analysis, partly due to its importance for long-term player tracking. Here we introduce a novel public jersey number recognition dataset for hockey and study how scene text recognition methods can be adapted to this problem. We demonstrate high performance on image- and tracklet-level tasks, achieving 91.4% accuracy for hockey images and 87.4% for soccer tracklets.
  arXiv  Detail & Related papers  (2024-05-22T18:08:26Z)
• 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)
• Jersey Number Recognition using Keyframe Identification from Low-Resolution Broadcast Videos [7.776923607006088]
  Player identification is a crucial component in vision-driven soccer analytics, enabling various tasks such as player assessment, in-game analysis, and broadcast evaluations. Previous methods have shown success in image data but struggle with real-world video data, where jersey numbers are not visible in most frames. We propose a robust downstream identification module that extracts frames containing essential high-level information about the jersey number.
  arXiv  Detail & Related papers  (2023-09-12T14:43:50Z)
• 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 Re-ID: Improving Re-Identification Of Players In Broadcast Videos Of Team Sports [0.0]
  This work focuses on player re-identification in broadcast videos of team sports. Specifically, we focus on identifying the same player in images captured from different camera viewpoints during any given moment of a match.
  arXiv  Detail & Related papers  (2022-06-06T06:06:23Z)
• Automated player identification and indexing using two-stage deep learning network [0.23610495849936355]
  We propose a deep learning-based player tracking system to automatically track players and index their participation per play in American football games. It is a two-stage network design to highlight areas of interest and identify jersey number information with high accuracy. We demonstrate the effectiveness and reliability of player tracking system by analyzing the qualitative and quantitative results on football videos.
  arXiv  Detail & Related papers  (2022-04-26T02:59:03Z)
• 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)
• Semantic-guided Pixel Sampling for Cloth-Changing Person Re-identification [80.70419698308064]
  This paper proposes a semantic-guided pixel sampling approach for the cloth-changing person re-ID task. We first recognize the pedestrian's upper clothes and pants, then randomly change them by sampling pixels from other pedestrians. Our method achieved 65.8% on Rank1 accuracy, which outperforms previous methods with a large margin.
  arXiv  Detail & Related papers  (2021-07-24T03:41:00Z)
• Apparel-invariant Feature Learning for Apparel-changed Person Re-identification [70.16040194572406]
  Most public ReID datasets are collected in a short time window in which persons' appearance rarely changes. In real-world applications such as in a shopping mall, the same person's clothing may change, and different persons may wearing similar clothes. It is critical to learn an apparel-invariant person representation under cases like cloth changing or several persons wearing similar clothes.
  arXiv  Detail & Related papers  (2020-08-14T03:49:14Z)
• Intra-Camera Supervised Person Re-Identification [87.88852321309433]
  We propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation. This eliminates the most time-consuming and tedious inter-camera identity labelling process. We formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method for Intra-Camera Supervised (ICS) person re-id.
  arXiv  Detail & Related papers  (2020-02-12T15:26:33Z)

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.