Automatic summarization of cricket video events using genetic algorithm

Narasimhan, Harikrishna; Satheesh, Sanjeev; Sriram, D. M. V. Satya

doi:10.1145/1830761.1830858

Cited by 7 publications

(1 citation statement)

References 13 publications

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“…Similar studies for sports such as basketball [6], baseball [7], and tennis [8] have also been reported. More recent work, in the domain of sports video summarization, has been reported in [12] and [13] for the game of cricket.…”

Section: A Automatic Highlights Generationmentioning

confidence: 99%

A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features

Ravi

Venugopal

Paul

et al. 2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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In recent years, there has been increased interest in video summarization and automatic sports highlights generation. In this work, we introduce a new dataset, called SNOW, for umpire pose detection in the game of cricket. The proposed dataset is evaluated as a preliminary aid for developing systems to automatically generate cricket highlights. In cricket, the umpire has the authority to make important decisions about events on the field. The umpire signals important events using unique hand signals and gestures. We identify four such events for classification namely SIX, NO BALL, OUT and WIDE based on detecting the pose of the umpire from the frames of a cricket video. Pre-trained convolutional neural networks such as Inception V3 and VGG19 networks are selected as primary candidates for feature extraction. The results are obtained using a linear SVM classifier. The highest classification performance was achieved for the SVM trained on features extracted from the VGG19 network. The preliminary results suggest that the proposed system is an effective solution for the application of cricket highlights generation.

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