DeepPlayer-Track: Player and Referee Tracking With Jersey Color Recognition in Soccer

Naik, Banoth Thulasya; Hashmi, Mohammad Farukh; Geem, Zong Woo; Bokde, Neeraj Dhanraj

doi:10.1109/access.2022.3161441

Cited by 23 publications

(9 citation statements)

References 46 publications

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“…Naik et al 37 introduced DeepPlayer-Track in 2022, which tracks players and referees through jersey color recognition, achieving an MOTA of 96% on the ISSIA dataset and 69% on the SoccerNet dataset. In 2024, Naik et al 38 proposed EIoU-distance loss, achieving an accuracy of 88% on the ISSIA dataset. Our tracker achieves an HOTA of 70.0% and MOTA of 87.5%on the SportsMOT dataset.…”

Section: Player Tracking Evaluationmentioning

confidence: 99%

Soccer match broadcast video analysis method based on detection and tracking

Li,

Yang,

Yang

et al. 2024

Computer Animation & Virtual

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We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two‐dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high‐speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non‐trivial. To mitigate this, we curate a large‐scale, high‐precision soccer ball detection dataset and devise a robust detection model, which achieved the of 80.9%. Additionally, we develop a high‐speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real‐time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations.

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Section: Player Tracking Evaluationmentioning

confidence: 99%

Soccer match broadcast video analysis method based on detection and tracking

Li,

Yang,

Yang

et al. 2024

Computer Animation & Virtual

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“…In such a case, a jersey number must be detected to recognize the player [60]. Accurate tracking [61][62][63][64][65][66][67][68][69][70][71][72] by detection [73][74][75][76] of multiple soccer players as well as the ball in real-time is a major challenge to evaluate the performance of the players, to find their relative positions at regular intervals, and to link spatiotemporal data to extract trajectories. The systems which evaluate the player [77] or team performance [78] have the potential to understand the game's aspects, which are not obvious to the human eye.…”

Section: Soccermentioning

confidence: 99%

“…This methodology effectively handles challenging situations, such as partial occlusions, players and the ball reappearing after a few frames, but fails when the players are severely occluded. [72] Player, referee and ball detection and tracking by jersey color recognition in soccer.…”

Section: Soccermentioning

confidence: 99%

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

2022

Self Cite

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Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports such as soccer, basketball, cricket, and badminton, studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications: high-level analysis such as detection and classification of players, tracking players or balls in sports and predicting the trajectories of players or balls, recognizing the team’s strategies, and classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researcher’s views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been discussed. This paper reviews detailed discussion on some of the artificial intelligence (AI) applications, GPU-based work-stations and embedded platforms in sports vision. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.

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“…Single object tracking algorithms became popular and gained interest in resent years because of its wide range of applications in computer vision, including video surveillance [3], augmented reality [4], automated driving [5], mobile robotics [6], traffic monitoring [7], sports video analysis [8], scene understanding [9], and human computer interaction [10]. Single object VOT approaches captures the target's appearance features in the first frame of a video sequence and then use it to locate the target in the remaining frames.…”

Section: Introductionmentioning

confidence: 99%

Transformers in Single Object Tracking: An Experimental Survey

Thangavel¹,

Thanikasalam²,

Ramanan³

et al. 2023

Preprint

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Single object tracking is a well-known and challenging research topic in computer vision. Over the last two decades, numerous researchers have proposed various algorithms to solve this problem and achieved promising results. Recently, Transformer-based tracking approaches have ushered in a new era in single object tracking due to their superior tracking robustness. Although several survey studies have been conducted to analyze the performance of trackers, there is a need for another survey study after the introduction of Transformers in single object tracking. In this survey, we aim to analyze the literature and performances of Transformer tracking approaches. Therefore, we conduct an in-depth literature analysis of Transformer tracking approaches and evaluate their tracking robustness and computational efficiency on challenging benchmark datasets. In addition, we have measured their performances on different tracking scenarios to find their strength and weaknesses. Our survey provides insights into the underlying principles of Transformer tracking approaches, the challenges they face, and their future directions.

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DeepPlayer-Track: Player and Referee Tracking With Jersey Color Recognition in Soccer

Cited by 23 publications

References 46 publications

Soccer match broadcast video analysis method based on detection and tracking

Soccer match broadcast video analysis method based on detection and tracking

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

Transformers in Single Object Tracking: An Experimental Survey

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