A Badminton Recognition and Tracking System Based on Context Multi-feature Fusion

• URL: http://arxiv.org/abs/2306.14492v1
• Date: Mon, 26 Jun 2023 08:07:56 GMT
• Title: A Badminton Recognition and Tracking System Based on Context Multi-feature Fusion
• Authors: Xinyu Wang and Jianwei Li
• Abstract summary: Two trajectory clip trackers are designed based on different rules to capture the correct trajectory of the ball. Two rounds of detection from coarse-grained to fine-grained are used to solve the challenges encountered in badminton detection.
• Score: 6.068573093901329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ball recognition and tracking have traditionally been the main focus of computer vision researchers as a crucial component of sports video analysis. The difficulties, such as the small ball size, blurry appearance, quick movements, and so on, prevent many classic methods from performing well on ball detection and tracking. In this paper, we present a method for detecting and tracking badminton balls. According to the characteristics of different ball speeds, two trajectory clip trackers are designed based on different rules to capture the correct trajectory of the ball. Meanwhile, combining contextual information, two rounds of detection from coarse-grained to fine-grained are used to solve the challenges encountered in badminton detection. The experimental results show that the precision, recall, and F1-measure of our method, reach 100%, 72.6% and 84.1% with the data without occlusion, respectively.

Related papers

• Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object Tracking [52.04679257903805]
  Joint Detection and Embedding (JDE) trackers have demonstrated excellent performance in Multi-Object Tracking (MOT) tasks. Our tracker, named TCBTrack, achieves state-of-the-art performance on multiple public benchmarks.
  arXiv  Detail & Related papers  (2024-07-19T07:48:45Z)
• An All Deep System for Badminton Game Analysis [0.0874967598360817]
  The CoachAI Badminton 2023 Track1 initiative aim to automatically detect events within badminton match videos. We've implemented various deep learning methods to tackle the problems arising from noisy detectied data. Our system garnered a score of 0.78 out of 1.0 in the challenge.
  arXiv  Detail & Related papers  (2023-08-24T08:41:40Z)
• 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)
• Table Tennis Stroke Detection and Recognition Using Ball Trajectory Data [5.735035463793008]
  A single camera setup positioned in the umpire's view has been employed to procure a dataset consisting of six stroke classes executed by four professional table tennis players. Ball tracking using YOLOv4, a traditional object detection model, and TrackNetv2, a temporal heatmap based model, have been implemented on our dataset. A mathematical approach developed to extract temporal boundaries of strokes using the ball trajectory data yielded a total of 2023 valid strokes. The temporal convolutional network developed performed stroke recognition on completely unseen data with an accuracy of 87.155%.
  arXiv  Detail & Related papers  (2023-02-19T19:13:24Z)
• Graph-Based Multi-Camera Soccer Player Tracker [1.6244541005112743]
  The paper presents a multi-camera tracking method intended for tracking soccer players in long shot video recordings from multiple calibrated cameras installed around the playing field. The large distance to the camera makes it difficult to visually distinguish individual players, which adversely affects the performance of traditional solutions. Our method focuses on individual player dynamics and interactions between neighborhood players to improve tracking performance.
  arXiv  Detail & Related papers  (2022-11-03T20:01:48Z)
• 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)
• MonoTrack: Shuttle trajectory reconstruction from monocular badminton video [6.218613353519723]
  We present the first complete end-to-end system for the extraction and segmentation of 3D shuttle trajectories from monocular badminton videos. Our system integrates badminton domain knowledge such as court dimension, shot placement, physical laws of motion, along with vision-based features such as player poses and shuttle tracking.
  arXiv  Detail & Related papers  (2022-04-04T23:57:57Z)
• Monocular Visual Analysis for Electronic Line Calling of Tennis Games [57.14812636359967]
  Electronic Line Calling is an auxiliary referee system used for tennis matches based on binocular vision technology. We propose a monocular vision technology based ELC method. We find out whether the bouncing point of the ball is out of bounds or not according to the relative position between the bouncing point and the court side line in the two dimensional image.
  arXiv  Detail & Related papers  (2021-07-20T04:23:11Z)
• Tracklets Predicting Based Adaptive Graph Tracking [51.352829280902114]
  We present an accurate and end-to-end learning framework for multi-object tracking, namely textbfTPAGT. It re-extracts the features of the tracklets in the current frame based on motion predicting, which is the key to solve the problem of features inconsistent.
  arXiv  Detail & Related papers  (2020-10-18T16:16:49Z)

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.