Automatic Soccer Player Tracking in Single Camera with Robust Occlusion Handling Using Attribute Matching

Sabirin, Houari; Suzuki, Hiroshi; Naito, Seiji

doi:10.1587/transinf.2014edp7313

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…In the other cases, occlusion and collision were the causes that lead to the disconnected tracks. This problem may be reduced (or even solved) using temporal correlation via attribute matching as proposed in [2], which can be tested in future work.…”

Section: Resultsmentioning

confidence: 99%

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Low Cost Player Tracking in Field Hockey

Moura

Kholkine

Damme

et al. 2022

Communications in Computer and Information Science

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In the paper, we describe the technical details of a multiplayer tracker system using tracking data obtained from a single low-cost stationary camera on field hockey games. Analyzing the tracking data of the players only from the transmitted video opens a multitude of applications that allows the cost of technology to be reduced. This method does not depend on the cooperation of the players (by using sensors) or their teams (by sharing data with a third party). The approach taken in this paper uses a variety of computer vision and tracking techniques. Making player tracking data more accessible lowers the barrier to entry for sports research and increases the period during which advanced analysis methods can be applied. The proposed system runs the full pipeline at 3 fps on a computer with a simple graphics card.

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Section: Resultsmentioning

confidence: 99%

“…AI is widely used to track players using video data. Most studies consider football [1], soccer [2,3], basketball [4,5], and ice hockey [6] games. Most of them require expensive hardware around the pitch, sensors attached to the players or the ball, and manual annotation.…”

Section: Introductionmentioning

confidence: 99%

Low Cost Player Tracking in Field Hockey

Moura

Kholkine

Damme

et al. 2022

Communications in Computer and Information Science

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“…In these methods preprocessing steps are simpler and faster, as they do not require time and location synchronization. However, as they need to cover the whole playfield, the frames are mostly blurry and difficult to use for detection Needham and Boyle [2001], Rodriguez-Canosa et al [2012], Sabirin et al [2015], Arbues et al [2019]. An alternative setting to improve resolution and accuracy is to use multiple fixed cameras.…”

Section: Eligibility and Data Collectionmentioning

confidence: 99%

Optical tracking in team sports

Rahimian,

Toka

2022

Preprint

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Sports analysis has gained paramount importance for coaches, scouts, and fans. Recently, computer vision researchers have taken on the challenge of collecting the necessary data by proposing several methods of automatic player and ball tracking. Building on the gathered tracking data, data miners are able to perform quantitative analysis on the performance of players and teams. With this survey, our goal is to provide a basic understanding for quantitative data analysts about the process of creating the input data and the characteristics thereof. Thus, we summarize the recent methods of optical tracking by providing a comprehensive taxonomy of conventional and deep learning methods, separately. Moreover, we discuss the preprocessing steps of tracking, the most common challenges in this domain, and the application of tracking data to sports teams. Finally, we compare the methods by their cost and limitations, and conclude the work by highlighting potential future research directions. Keywords sports analytics • player recognition • player tracking • optical tracking • image processing • deep learning

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“…Some strategies analyze the histogram of the hue (H) component of the HSV color space in search of the dominant mode [14,15,16,17,18], which is assumed to correspond to the green color of the field of play. By separating this mode from the rest of the histogram data, high quality results are typically obtained.…”

Section: Field Of Play Segmentationmentioning

confidence: 99%

Soccer line mark segmentation and classification with stochastic watershed transform

Berjón¹,

Cuevas²,

García³

2021

Preprint

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Augmented reality applications are beginning to change the way sports are broadcast, providing richer experiences and valuable insights to fans. The first step of augmented reality systems is camera calibration, possibly based on detecting the line markings of the field of play. Most existing proposals for line detection rely on edge detection and Hough transform, but optical distortion and extraneous edges cause inaccurate or spurious detections of line markings. We propose a novel strategy to automatically and accurately segment line markings based on a stochastic watershed transform that is robust to optical distortions, since it makes no assumptions about line straightness, and is unaffected by the presence of players or the ball in the field of play. Firstly, the playing field as a whole is segmented completely eliminating the stands and perimeter boards. Then the line markings are extracted.The strategy has been tested on a new and public database composed by 60 annotated images from matches in five stadiums. The results obtained have proven that the proposed segmentation algorithm allows successful and precise detection of most line mark pixels.

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Automatic Soccer Player Tracking in Single Camera with Robust Occlusion Handling Using Attribute Matching

Cited by 11 publications

References 23 publications

Low Cost Player Tracking in Field Hockey

Low Cost Player Tracking in Field Hockey

Optical tracking in team sports

Soccer line mark segmentation and classification with stochastic watershed transform

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