Statistical analyzing method of camera motion parameters for categorizing sports video

Takagi, Shinichi; Hattori, Shinobu; Yokoyama, Kenichi; Kodate, Akihisa; Tominaga, H.

doi:10.1049/cp:20030527

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…S.Takagi. et al [6] proposed two features of statistical analysis method to categorize the sport video. These two features are the "Camera motion extraction ratio" and "Camera motion transition".…”

Section: Literature Reviewmentioning

confidence: 99%

Computer vision approach to automatic linesman

Leong

Zulkifley

Hussain

2014

2014 IEEE 10th International Colloquium on Signal Processing and Its Applications

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To date, there is no implementation of an automatic linesman system (ALS) in any official badminton game. An ALS is important in assisting the game umpire/referee to make a better judgment in a dispute. Just like the Hawkeye system in tennis, the ALS is anticipated to play more important role in big games that require high standard refereeing quality like the Olympic, Badminton World Championships (BWF) etc. This paper proposed an algorithm to model the shuttlecock position so as to track and determine its landing point either inside or outside the boundaries of the court line. It involves background subtraction method in hue space, analysis of the shuttlecock trajectory and a decision making process to determine the shuttlecock landing point. Performance is evaluated based on three metrics namely, accuracy, sensitivity and specificity. Initial results showed that the proposed algorithm works well with low error values. However, further works is required to refine the proposed ALS before it can be fully implemented in an official game.

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Section: Literature Reviewmentioning

confidence: 99%

Computer vision approach to automatic linesman

Leong

Zulkifley

Hussain

2014

2014 IEEE 10th International Colloquium on Signal Processing and Its Applications

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“…Categorizing sports video by statistical analyzing and camera motion parameters is discussed in [7]. The video frames are extracted by camera motion parameters like camera motion transition and camera motion extraction ratio.…”

Section: Introductionmentioning

confidence: 99%

Sports Video Categorization by Multiclass SVM Using Higher Order Spectra Features

Mohanan¹

2017

International Journal of Advances in Signal and Image Sciences

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Video classification is one of the rising fields in the video analysis. A large number of videos are accessed by people's daily from television and internet. It is easy for humans to index the video from the collection of videos which contains news, cartoon, sports, comedy and drama. Among the categories, sports video plays a vital role due to their commercial demand. There is a similarity between the different sports video which makes the classification task difficult. In this study, the sports video categorization for five categories of sports like football, cricket, volleyball, tennis and basketball is presented. The sports video categorization system uses Higher Order Spectra Features (HOSF) for the feature extraction from video frames and multiclass Support Vector Machine (SVM) classifier for the classification of videos. The system gives average classification accuracy of 93.44% using HOSF and multiclass SVM classifier.

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Statistical analyzing method of camera motion parameters for categorizing sports video

Cited by 2 publications

References 7 publications

Computer vision approach to automatic linesman

Computer vision approach to automatic linesman

Sports Video Categorization by Multiclass SVM Using Higher Order Spectra Features

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