SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos

Scott, Atom; Uchida, Ikuma; Onishi, Masaki; Kameda, Yoshinari; Fukui, Kazuhiro; Fujii, Keisuke

doi:10.1109/cvprw56347.2022.00401

Cited by 28 publications

(2 citation statements)

References 37 publications

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“…More recently, reidentification-based trackers, like StrongSORT [12], have emerged, which focus on extracting more discriminative features for object re-identification to improve tracking results [2,29,57]. Some works have also been specifically developed for tracking team sport players [16,22,30,32,33]. [55] proposes a deep learning-based approach for multi-camera multi-player tracking in sports videos, leveraging deep player identification to improve tracking accuracy and consistency across multiple cameras.…”

Section: Multiple Object Trackingmentioning

confidence: 99%

Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking

Mansourian,

Somers,

De Vleeschouwer

et al. 2023

Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports

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Effective tracking and re-identification of players is essential for analyzing soccer videos. But, it is a challenging task due to the nonlinear motion of players, the similarity in appearance of players from the same team, and frequent occlusions. Therefore, the ability to extract meaningful embeddings to represent players is crucial in developing an effective tracking and re-identification system. In this paper, a multi-purpose part-based person representation method, called PRTreID, is proposed that performs three tasks of role classification, team affiliation, and re-identification, simultaneously. In contrast to available literature, a single network is trained with multi-task supervision to solve all three tasks, jointly. The proposed joint method is computationally efficient due to the shared backbone. Also, the multi-task learning leads to richer and more discriminative representations, as demonstrated by both quantitative and qualitative results. To demonstrate the effectiveness of PRTreID, it is integrated with a state-of-the-art tracking method, using a part-based post-processing module to handle long-term tracking. The proposed tracking method, outperforms all existing tracking methods on the challenging SoccerNet tracking dataset. CCS CONCEPTS• Computing methodologies → Tracking.

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Section: Multiple Object Trackingmentioning

confidence: 99%

Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking

Mansourian,

Somers,

De Vleeschouwer

et al. 2023

Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports

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“…Besides, broadcast videos also suffer from severe occlusion, especially in team sports. In soccer, drone cameras are used to capture the entire pitch in a single frame [31], providing greater adaptability and flexibility. Unfortunately, there is currently no such dataset for any racket sports.…”

mentioning

confidence: 99%

Estimation of control area in badminton doubles with pose information from top and back view drone videos

Ding,

Takeda,

Jin

et al. 2023

Multimed Tools Appl

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The application of visual tracking to the performance analysis of sports players in dynamic competitions is vital for effective coaching. In doubles matches, coordinated positioning is crucial for maintaining control of the court and minimizing opponents’ scoring opportunities. The analysis of such teamwork plays a vital role in understanding the dynamics of the game. However, previous studies have primarily focused on analyzing and assessing singles players without considering occlusion in broadcast videos. These studies have relied on discrete representations, which involve the analysis and representation of specific actions (e.g., strokes) or events that occur during the game while overlooking the meaningful spatial distribution. In this work, we present the first annotated drone dataset from top and back views in badminton doubles and propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance. We present an efficient framework of deep neural networks that enables the calculation of full probability surfaces. This framework utilizes the embedding of a Gaussian mixture map of players’ positions and employs graph convolution on their poses. In the experiment, we verify our approach by comparing various baselines and discovering the correlations between the score and control area. Additionally, we propose a practical application for assessing optimal positioning to provide instructions during a game. Our approach offers both visual and quantitative evaluations of players’ movements, thereby providing valuable insights into doubles teamwork. The dataset and related project code is available at https://github.com/Ning-D/Drone_BD_ControlArea

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Evaluation of Creating Scoring Opportunities for Teammates in Soccer via Trajectory Prediction

Teranishi

Tsutsui

Takeda

et al. 2023

Communications in Computer and Information Science

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SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos

Cited by 28 publications

References 37 publications

Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking

Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking

Estimation of control area in badminton doubles with pose information from top and back view drone videos

Evaluation of Creating Scoring Opportunities for Teammates in Soccer via Trajectory Prediction

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