Player Tracking and Identification in Ice Hockey

Vats, Kanav; Walters, Pascale; Fani, Mehrnaz; Clausi, David A.; Zelek, John

doi:10.48550/arxiv.2110.03090

Cited by 6 publications

(11 citation statements)

References 43 publications

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“…Early research on player identification relied on hand-engineered features [31,32]. Though it is possible to identify players by their bodies, doing so by jersey number instead is more popular because players' numbers do not change, and are typically observable, throughout the game [33]. Multiple works have utilized CNNs for this purpose [1,34].…”

Section: Player Jersey Number Recognitionmentioning

confidence: 99%

Automatic Team Assignment and Jersey Number Recognition in Football Videos

Alhejaily¹,

Alhejaily²,

Almdahrsh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Football is one of the most-watched sports, but analyzing players' performance is currently difficult and labor intensive. Performance analysis is done manually, which means that someone must watch video recordings and then log each player's performance. This includes the number of passes and shots taken by each player, the location of the action, and whether or not the play had a successful outcome. Due to the time-consuming nature of manual analyses, interest in automatic analysis tools is high despite the many interdependent phases involved, such as pitch segmentation, player and ball detection, assigning players to their teams, identifying individual players, activity recognition, etc. This paper proposes a system for developing an automatic video analysis tool for sports. The proposed system is the first to integrate multiple phases, such as segmenting the field, detecting the players and the ball, assigning players to their teams, and identifying players' jersey numbers. In team assignment, this research employed unsupervised learning based on convolutional autoencoders (CAEs) to learn discriminative latent representations and minimize the latent embedding distance between the players on the same team while simultaneously maximizing the distance between those on opposing teams. This paper also created a highly accurate approach for the real-time detection of the ball. Furthermore, it also addressed the lack of jersey number datasets by creating a new dataset with more than 6,500 images for numbers ranging from 0 to 99. Since achieving a high performance in deep learning requires a large training set, and the collected dataset was not enough, this research utilized transfer learning (TL) to first pretrain the jersey number detection model on another large dataset and then fine-tune it on the target dataset to increase the accuracy. To test the proposed system, this paper presents a comprehensive evaluation of its individual stages as well as of the system as a whole. All codes, datasets, and experiments are available on GitHub (https://github.com/ragadalhejaily/project).

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Section: Player Jersey Number Recognitionmentioning

confidence: 99%

Automatic Team Assignment and Jersey Number Recognition in Football Videos

Alhejaily¹,

Alhejaily²,

Almdahrsh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…Several methods tracks players by using re-ID features [24,34,47,51,54]. Lu et al [34] use DPM [13] to detect basketball players.…”

Section: Tracking With Re-identificationmentioning

confidence: 99%

“…Kong et al [28] mix player appearance, posture and motion criteria to match new detections with existing tracks. Vats et al [47] use a Faster R-CNN network [40] to detect hockey players and a batch method for tracking [5]. Specific ResNet-18 networks [19] are used to identify the player teams and jersey numbers.…”

Section: Tracking With Re-identificationmentioning

confidence: 99%

“…The availability of large public datasets and challenges such as the MOT challenge [38] allows to fairly train and compare the various approaches. Some recent people tracking methods have also been proposed for the specific context of team sports: soccer [24,54], basketball [34] and hockey [47]. These methods often use private datasets specific to their studied sport to get competitive results evaluated on short video clips extracted from a match.…”

Section: Introductionmentioning

confidence: 99%

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Efficient tracking of team sport players with few game-specific annotations

Maglo

Orcesi

Pham

2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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One of the requirements for team sports analysis is to track and recognize players. Many tracking and reidentification methods have been proposed in the context of video surveillance. They show very convincing results when tested on public datasets such as the MOT challenge. However, the performance of these methods are not as satisfactory when applied to player tracking. Indeed, in addition to moving very quickly and often being occluded, the players wear the same jersey, which makes the task of reidentification very complex. Some recent tracking methods have been developed more specifically for the team sport context. Due to the lack of public data, these methods use private datasets that make impossible a comparison with them. In this paper, we propose a new generic method to track team sport players during a full game thanks to few human annotations collected via a semi-interactive system. Non-ambiguous tracklets and their appearance features are automatically generated with a detection and a reidentification network both pre-trained on public datasets. Then an incremental learning mechanism trains a Transformer to classify identities using few game-specific human annotations. Finally, tracklets are linked by an association algorithm. We demonstrate the efficiency of our approach on a challenging rugby sevens dataset. To overcome the lack of public sports tracking dataset, we publicly release this dataset at https://kalisteo.cea.fr/ index.php/free-resources/. We also show that our method is able to track rugby sevens players during a full match, if they are observable at a minimal resolution, with the annotation of only 6 few seconds length tracklets per player.

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“…Player Tracklet [207] comprises 84 video clips from broadcast NHL games and the average length of the videos is 36s. The positions of players and referee in each frame are annotated with bounding boxes and identity labels like players' names and numbers.…”

Section: Hockeymentioning

confidence: 99%

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Wu¹,

Wang²,

Bian³

et al. 2022

Preprint

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To understand human behaviors, action recognition based on videos is a common approach. Compared with imagebased action recognition, videos provide much more information. Reducing the ambiguity of actions and in the last decade, many works focused on datasets, novel models and learning approaches have improved video action recognition to a higher level. However, there are challenges and unsolved problems, in particular in sports analytics where data collection and labeling are more sophisticated, requiring sport professionals to annotate data. In addition, the actions could be extremely fast and it becomes difficult to recognize them. Moreover, in team sports like football and basketball, one action could involve multiple players, and to correctly recognize them, we need to analyse all players, which is relatively complicated. In this paper, 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, tennis, diving and badminton. Then we compare numerous existing frameworks for sports analysis to present status quo of video action recognition in both team sports and individual sports. Finally, we discuss the challenges and unsolved problems in this area and to facilitate sports analytics, we develop a toolbox using PaddlePaddle 1 , which supports football, basketball, table tennis and figure skating action recognition.

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Player Tracking and Identification in Ice Hockey

Cited by 6 publications

References 43 publications

Automatic Team Assignment and Jersey Number Recognition in Football Videos

Automatic Team Assignment and Jersey Number Recognition in Football Videos

Efficient tracking of team sport players with few game-specific annotations

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

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