Soccer video segmentation: Referee and player detection

Nunez, J.R.; Facon, Jacques; Brito, Alceu De S.

doi:10.1109/iwssip.2008.4604421

Cited by 11 publications

(5 citation statements)

References 4 publications

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“…Automatic annotation of broadcast sport videos is an interesting area for researchers. Some approaches deal with ball and field detection [1], and many papers focus on player detection and tracking [2,3,4,7,10]. The approach in [2] is a semi automatic approach for tracking and detecting an important player in a shot.…”

Section: Related Workmentioning

confidence: 99%

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Video annotation with metadata: A case study of soccer game video annotation with players

2022

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In recent years, the use of convolutional neural networks for video processing has become very attractive. The reason lies in the computational power for data processing which is available today. There are many well-defined research areas where neural networks have brought higher reliability than other conventional approaches; for example, traffic sign recognition and isolated number recognition. In this paper, we will describe the architecture and the implementation of the process of soccer game annotation. The game is annotated with data about players. The technology of convolutional neural networks is used for number recognition. The process runs in real-time on a streaming video. Content enriched with metadata is given to the user in parallel with the real-time video. In the paper, we will describe in some detail the following modules: Image binarization, shot localization, the selection and recognition of numbers on players` jerseys.

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Section: Related Workmentioning

confidence: 99%

“…In [3], player regions are detected using Multilayer Perceptron Neural Network as the classifier for player and non-player regions. The approach in [4] uses dominant colour region detection for player detection. HOG features combined with an SVM classifier are used for player detection in [7].…”

Section: Related Workmentioning

confidence: 99%

Video annotation with metadata: A case study of soccer game video annotation with players

2022

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“…While background subtraction based approaches are often used for sports player detection [1,2,3], feature based classification methods are more popular in general person detection [4]. Nunez et al [1] create a binary field mask by applying two thresholds on the hue component of a video frame in the HSV color space and using morphological operations to re duce noise. The remaining blobs are analyzed with color histograms in a rule-based manner to remove non-player blobs.…”

Section: Adaptation Detectionmentioning

confidence: 99%

Unsupervised color classifier training for soccer player detection

Gerke

Singh

Linnemann

et al. 2013

2013 Visual Communications and Image Processing (VCIP)

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Player detection in sports video is a challenging task: In contrast to typical surveillance applications, a pan-tilt-zoom camera model is used. Therefore, simple background learning approaches cannot be used. Furthermore, camera motion causes severe motion blur, making gradient based approaches less robust than in settings where the camera is static. The contribution of this paper is a sequence adaptive approach that utilizes color information in an unsupervised manner to improve detection accuracy. Therefore, different color features, namely color histograms, color spatiograms and a color and edge directivity descriptor are evaluated. It is shown that the proposed color adaptive approach improves detection accuracy. In terms of maximum F1 score, an improvement from 0.79 to 0.81 is reached using block-wise HSV histograms. The average number of false positives per image (FPPI) at two fixed recall levels decreased by approximately 23%

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“…Initially we compute only the covariance of the whole region, C 1 , from the template image. We search a target image for a region having similar covariance feature by a brute force search, and the dissimilarity is measured through (4) . At all the locations in the target image we analyze at nine different scales (four smaller, four larger) to find matching regions.…”

Section: Template Matchingmentioning

confidence: 99%

A comparison with covariance features on player uniform number recognition

Ando

Ohki

Fujita

2011

2011 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)

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Broadcast sports video report is expected be the generated player data in real time. Therefore, uniform number recognition need to fast computing. Covariance feature has possibilities to do it in real time. We present a method for uniform number recognition using the covariance feature as a region descriptor. The covariance feature is represented by the covariance matrix of image features such as spatial position, intensity, higher order derivatives, etc. Thus, the covariance feature vary by used image features. Feature matching is a simple nearest neighbor search under a distance measure [1]. In the experiments, the six covariances compared. The recognition rate is 79% in a multiple player game.

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Soccer video segmentation: Referee and player detection

Cited by 11 publications

References 4 publications

Video annotation with metadata: A case study of soccer game video annotation with players

Video annotation with metadata: A case study of soccer game video annotation with players

Unsupervised color classifier training for soccer player detection

A comparison with covariance features on player uniform number recognition

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