Ground Sports Strategy Formulation and Assistance Technology Develpoment: Player Data Acquisition from Drone Videos

Karungaru, Stephen; Matsuura, Kenji; Tanioka, Hiroki; Wada, Tomohito; Gotoda, Naka

doi:10.1109/icitm.2019.8710735

Cited by 7 publications

(4 citation statements)

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“…This study is the first to demonstrate the application of a drone-based video system for the performance analysis of three different sports: tennis, Ultimate Frisbee, and soccer. The results not only show the system’s ability to detect and track players from a bird’s eye view ( Ferreira et al, 2015 ; Karungaru et al, 2019 ), but also to collect and generate position detection data. Furthermore, the results from this study are validated against some of the existing position detection technologies (GPS and LPS) that are currently used in performance analysis.…”

Section: Discussionmentioning

confidence: 95%

“…However, these methods all rely on multiple fixed cameras, and none have yet made use of a single drone camera. Concerning the use of drones in sports, Ferreira et al (2015) and Karungaru et al (2019) report that it is possible to detect and track players using a drone, but neither of these studies investigated its use for performance analysis. These studies also failed to validate the accuracy and reliability of the positional data obtained from the drone footage.…”

Section: Introductionmentioning

confidence: 99%

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Drone-Based Position Detection in Sports—Validation and Applications

et al. 2022

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Radio and video-based electronic performance and tracking systems (EPTS) for position detection are widely used in a variety of sports. In this paper, the authors introduce an innovative approach to video-based tracking that uses a single camera attached to a drone to capture an area of interest from a bird’s eye view. This pilot validation study showcases several applications of this novel approach for the analysis of game and racket sports. To this end, the authors compared positional data retrieved from video footage recorded using a drone with positional data obtained from established radio-based systems in three different setups: a tennis match during training with the drone hovering at a height of 27 m, a small-sided soccer game with the drone at a height of 50 m, and an Ultimate Frisbee match with the drone at a height of 85 m. For each type of playing surface, clay (tennis) and grass (soccer and Ultimate), the drone-based system demonstrated acceptable static accuracy with root mean square errors of 0.02 m (clay) and 0.15 m (grass). The total distance measured using the drone-based system showed an absolute difference of 2.78% in Ultimate and 2.36% in soccer, when compared to an established GPS system and an absolute difference of 2.68% in tennis, when compared to a state-of-the-art LPS. The overall ICC value for consistency was 0.998. Further applications of a drone-based EPTS and the collected positional data in the context of performance analysis are discussed. Based on the findings of this pilot validation study, we conclude that drone-based position detection could serve as a promising alternative to existing EPTS but would benefit from further comparisons in dynamic settings and across different sports.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Drone-Based Position Detection in Sports—Validation and Applications

et al. 2022

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“…Limitations and Remarks [213] Court detection using markers, player detection, and tracking using a drone.…”

Section: Problem Statement Proposed Methodology Precision and Perform...mentioning

confidence: 99%

“…In sports such as table tennis [200], squash [201,202], and golf [203], ball detection and tracking and player pose detection [204] are challenging tasks. In ball-centric sports such as rugby, American football, handball, baseball, ball/player detection [205][206][207][208][209][210][211] and tracking [212][213][214][215][216][217][218][219][220][221], analyzing the action of the player [23,[222][223][224][225][226][227], event detection and classification [228][229][230][231][232], performance analysis of player [233][234][235], referee identification and gesture recognition are the major challenging tasks. Video highlight generation is a subclass of video summarization [236][237][238][239] which may be viewed as a subclass of sports video analysis.…”

Section: Miscellaneousmentioning

confidence: 99%

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

2022

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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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Drones as internet of video things front-end sensors: challenges and opportunities

Chen

2021

Discov Internet Things

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Internet of Video Things (IoVT) has become an emerging class of IoT systems that are equipped with visual sensors at the front end. Most of such visual sensors are fixed one whereas the drones are considered flying IoT nodes capable of capturing visual data continuously while flying over the targets of interest. With such a dynamic operational mode, we can imagine significant technical challenges in sensor data acquisition, information transmission, and knowledge extraction. This paper will begin with an analysis on some unique characteristics of IoVT systems with drones as its front end sensors. We shall then discuss several inherent technical challenges for designing drone-based IoVT systems. Furthermore, we will present major opportunities to adopt drone-based IoVT in several contemporary applications. Finally, we conclude this paper with a summary and an outlook for future research directions.

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Ground Sports Strategy Formulation and Assistance Technology Develpoment: Player Data Acquisition from Drone Videos

Cited by 7 publications

References 4 publications

Drone-Based Position Detection in Sports—Validation and Applications

Drone-Based Position Detection in Sports—Validation and Applications

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

Drones as internet of video things front-end sensors: challenges and opportunities

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