Towards Real-Time Analysis of Broadcast Badminton Videos

• URL: http://arxiv.org/abs/2308.12199v1
• Date: Wed, 23 Aug 2023 15:38:26 GMT
• Title: Towards Real-Time Analysis of Broadcast Badminton Videos
• Authors: Nitin Nilesh, Tushar Sharma, Anurag Ghosh, C. V. Jawahar
• Abstract summary: We propose an end-to-end framework for player movement analysis for badminton matches on live broadcast match videos. Unlike other approaches which use multi-modal sensor data, our approach uses only visual cues. Our framework was successfully used to analyze live broadcast matches in real-time during the Premier Badminton League 2019.
• Score: 29.633481528698844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Analysis of player movements is a crucial subset of sports analysis. Existing player movement analysis methods use recorded videos after the match is over. In this work, we propose an end-to-end framework for player movement analysis for badminton matches on live broadcast match videos. We only use the visual inputs from the match and, unlike other approaches which use multi-modal sensor data, our approach uses only visual cues. We propose a method to calculate the on-court distance covered by both the players from the video feed of a live broadcast badminton match. To perform this analysis, we focus on the gameplay by removing replays and other redundant parts of the broadcast match. We then perform player tracking to identify and track the movements of both players in each frame. Finally, we calculate the distance covered by each player and the average speed with which they move on the court. We further show a heatmap of the areas covered by the player on the court which is useful for analyzing the gameplay of the player. Our proposed framework was successfully used to analyze live broadcast matches in real-time during the Premier Badminton League 2019 (PBL 2019), with commentators and broadcasters appreciating the utility.

Related papers

• Action Anticipation from SoccerNet Football Video Broadcasts [84.87912817065506]
  We introduce the task of action anticipation for football broadcast videos. We predict future actions in unobserved future frames within a five- or ten-second anticipation window. Our work will enable applications in automated broadcasting, tactical analysis, and player decision-making.
  arXiv  Detail & Related papers  (2025-04-16T12:24:33Z)
• BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports [0.0]
  We introduce a novel video segmentation strategy to extract frames of each player's racket swing in a badminton broadcast match. We propose Badminton Stroke-type Transformer (BST) to classify player stroke-types in singles.
  arXiv  Detail & Related papers  (2025-02-28T14:18:39Z)
• Tracking Players in a Badminton Court by Two Cameras [8.394650183250974]
  This study proposes a simple method for multi-object tracking (MOT) of players in a badminton court. We leverage two off-the-shelf cameras, one on the top of the court and the other on the side of the court.
  arXiv  Detail & Related papers  (2023-08-09T11:10:11Z)
• 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)
• 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)
• 6MapNet: Representing soccer players from tracking data by a triplet network [19.343859572602558]
  We build a triplet network named 6MapNet that can effectively capture the movement styles of players using in-game GPS data. Ourworks then map these heatmap pairs into feature vectors whose similarity corresponds to the actual similarity of playing styles.
  arXiv  Detail & Related papers  (2021-09-10T07:57:12Z)
• 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)
• 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)
• A Hybrid Approach for Tracking Individual Players in Broadcast Match Videos [1.160208922584163]
  This paper introduces a player tracking solution which is both fast and accurate. The approach combines several models that are executed concurrently in a relatively modest hardware. As for performance, our proposal can process high definition videos (1920x1080) at 80 fps.
  arXiv  Detail & Related papers  (2020-03-06T15:16:23Z)

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.