Related papers: Evaluating deep tracking models for player tracking in broadcast ice hockey video

Evaluating deep tracking models for player tracking in broadcast ice hockey video

URL: http://arxiv.org/abs/2205.10949v1
Date: Sun, 22 May 2022 22:56:31 GMT
Title: Evaluating deep tracking models for player tracking in broadcast ice hockey video
Authors: Kanav Vats, Mehrnaz Fani, David A. Clausi, John S. Zelek
Abstract summary: Player tracking is a challenging problem since the motion of players in hockey is fast-paced and non-linear. We compare and contrast several state-of-the-art tracking algorithms and analyze their performance and failure modes in ice hockey.
Score: 20.850267622473176
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: Tracking and identifying players is an important problem in computer vision based ice hockey analytics. Player tracking is a challenging problem since the motion of players in hockey is fast-paced and non-linear. There is also significant player-player and player-board occlusion, camera panning and zooming in hockey broadcast video. Prior published research perform player tracking with the help of handcrafted features for player detection and re-identification. Although commercial solutions for hockey player tracking exist, to the best of our knowledge, no network architectures used, training data or performance metrics are publicly reported. There is currently no published work for hockey player tracking making use of the recent advancements in deep learning while also reporting the current accuracy metrics used in literature. Therefore, in this paper, we compare and contrast several state-of-the-art tracking algorithms and analyze their performance and failure modes in ice hockey.

Related papers

Multi Player Tracking in Ice Hockey with Homographic Projections [13.320838012645444]
Multi Object Tracking (MOT) in ice hockey pursues the combined task of localizing and associating players across a given sequence to maintain their identities. We propose a novel tracking approach by formulating MOT as a bipartite graph matching problem infused with homography. We disentangle the positional representations of occluded and overlapping players in broadcast view, by mapping their foot keypoints to an overhead rink template, and encode these projected positions into the graph network.
arXiv Detail & Related papers (2024-05-22T07:14:55Z)
Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling [70.54015529131325]
Basketball is dictated by many variables, such as playstyle and game dynamics. It is crucial that the performance profiles can reflect the diverse playstyles. We present a tool that can visualize player performance profiles in a timely manner.
arXiv Detail & Related papers (2023-03-17T01:13:18Z)
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)
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)
SoccerNet 2022 Challenges Results [167.6158475931228]
SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations.
arXiv Detail & Related papers (2022-10-05T16:12:50Z)
Observation Centric and Central Distance Recovery on Sports Player Tracking [24.396926939889532]
We propose a motionbased tracking algorithm and three post-processing pipelines for three sports including basketball, football, and volleyball. Our method achieves a HOTA of 73.968, ranking 3rd place on the 2022 Sportsmot workshop final leaderboard.
arXiv Detail & Related papers (2022-09-27T04:48:11Z)
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)
Player Tracking and Identification in Ice Hockey [9.577770317771087]
This paper introduces an automated system to track and identify players in broadcast NHL hockey videos. The system is composed of three components (1) Player tracking, (2) Team identification and (3) Player identification. For team identification, the away-team jerseys are grouped into a single class and home-team jerseys are grouped in classes according to their jersey color. A novel player identification model is introduced that utilizes a temporal one-dimensional convolutional network to identify players from player bounding box sequences.
arXiv Detail & Related papers (2021-10-06T22:37:08Z)
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors [12.071865017583502]
We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network. The proposed solution has a number of promising applications for Pro eSports teams as well as a learning tool for amateur players.
arXiv Detail & Related papers (2020-12-07T07:31:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.