Adaptive pattern recognition in real-time video-based soccer analysis

Schlipsing, Marc; Salmen, Jan; Tschentscher, Marc; Igel, Christian

doi:10.1007/s11554-014-0406-1

Cited by 22 publications

(14 citation statements)

References 27 publications

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“…Apart from these autonomously working solution, few other approaches with the same level of sophistication exist. The work by Schlipsing et al [24] looked initially promising, but was abandoned after the principal investigator completed his Ph.D. Some other approaches exist, like Andrade et al [1], Liu et al [18], Wan et al [31], Xue et al [32].…”

Section: Related Workmentioning

confidence: 99%

“…Combining Video and Movement Data to Enhance Team Sport Analysis. The approach presented by Stein et al [27] is somewhat similar to the main idea followed by Schlipsing et al [24], but differs in two fundamental ways in the implementation: First, it aims to use already existing video recordings-like a scouting feed or simple television recordings-as a video source, instead of a carefully calibrated dual-camera setup. Secondly, the tracking of the players aims to be autonomous and not require manual input.…”

Section: Overview Of Current Approachesmentioning

confidence: 99%

“…This subcategory of insertion techniques contains approaches which actually embed the content into the 3D scene and respect relevant aspects like camera distortion and perspective projections. Approximately half of the approaches can be considered of this type, namely the ones by Liu et al [18], Schlipsing et al [24], both from Stein et al [26,27], Piero [22], and Viz Libero [30]. The four latter ones are the most sophisticated, leading to aesthetically pleasing results.…”

Section: Categorization and Comparisonmentioning

confidence: 99%

“…Several of the presented systems are capable of automatically placing the content at the desired position, also different levels of accuracy and difficulty exist. While this is one of the main aspects for reconstructive techniques (merge different images for a 3D reconstruction), the approaches by Wan et al [31], Andrade et al [1], and Liu et al [18] follow a simple positioning and only the approaches by Schlipsing et al [24] and Stein et al [26,27] are more advanced, with the latter doing a better job at the embedding.…”

Section: Categorization and Comparisonmentioning

confidence: 99%

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Video-based Analysis of Soccer Matches

Fischer

Keim

Stein

2019

Proceedings Proceedings of the 2nd International Workshop on Multimedia Content Analysis in Sports

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With the increasingly detailed investigation of game play and tactics in invasive team sports such as soccer, it becomes ever more important to present causes, actions and findings in a meaningful manner. Visualizations, especially when augmenting relevant information directly inside a video recording of a match, can significantly improve and simplify soccer match preparation and tactic planning. However, while many visualization techniques for soccer have been developed in recent years, few have been directly applied to the video-based analysis of soccer matches. This paper provides a comprehensive overview and categorization of the methods developed for the video-based visual analysis of soccer matches. While identifying the advantages and disadvantages of the individual approaches, we identify and discuss open research questions, soon enabling analysts to develop winning strategies more efficiently, do rapid failure analysis or identify weaknesses in opposing teams.

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Section: Related Workmentioning

confidence: 99%

Section: Overview Of Current Approachesmentioning

confidence: 99%

Section: Categorization and Comparisonmentioning

confidence: 99%

Section: Categorization and Comparisonmentioning

confidence: 99%

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Video-based Analysis of Soccer Matches

Fischer

Keim

Stein

2019

Proceedings Proceedings of the 2nd International Workshop on Multimedia Content Analysis in Sports

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“…In addition to aforementioned clustering techniques, colour table generalization method based on Support Vector Machines (SVM) classification model was suggested in order to organize the clusters. This is achieved by removing extreme value, filling the holes within the clusters and making the shape and boundaries of a cluster smoother [15][16][17][18]. Budden's work concludes that the optimal method for automatic pixels clustering is the Kmeans algorithm without adopting SVM.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Automatic Colour Calibration for NAO Humanoids

Bolotnikova

Anbarjafari

2018

Teh. vjesn.

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A challenge in NAO soccer robots is colour calibration. Good colour calibration can result in robust and accurate self-localization of the robot. Currently manual calibration is the only solution, which is used. In this paper, we are proposing an automatic real-time, accurate YUV colour space based colour calibration technique. In order to define average values for the desired colour classes namely orange, white, green and purple, a specified set of frames from the NAO camera are analysed. These average values are corrected by luminance analysis of a new frame and are passed to the K-means clustering algorithm as a set of initial means. In addition to these four values, a set of initial means of the K-means algorithm contains 16 values that are calculated in the following manner: the frame being processed is divided into 4 by 4 grids and the average value from every grid serves as an initial mean for K-means clustering. Consequently, colours of a similar type are combined into clusters. The final step of the proposed technique is cluster classification in which the average values of the desired colour classes are corrected by luminance analysis. As NAO cameras provide video streams in YUV format and the proposed algorithm uses this format there is no need for additional computational steps for conversation between the colour spaces. As a result, computational process is reduced compared to current techniques.

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