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

Aravind Ravi; Harshwin Venugopal; Sruthy Paul; Hamid R. Tizhoosh

doi:10.1109/SSCI.2018.8628877

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

Aravind Ravi, Harshwin Venugopal, Sruthy Paul, Hamid R. Tizhoosh

Artificial Intelligence and Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
Editors	Suresh Sundaram
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1396-1402
Number of pages	7
ISBN (Electronic)	9781538692769
DOIs	https://doi.org/10.1109/SSCI.2018.8628877
State	Published - Jul 2 2018
Event	8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India Duration: Nov 18 2018 → Nov 21 2018

Publication series

Name	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

Conference

Conference	8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Country/Territory	India
City	Bangalore
Period	11/18/18 → 11/21/18

Keywords

cricket
deep convolutional networks
image classification
inceptionv3
transfer learning
vgg19
video summarization

ASJC Scopus subject areas

Artificial Intelligence
Theoretical Computer Science

Access to Document

10.1109/SSCI.2018.8628877

Cite this

Ravi, A., Venugopal, H., Paul, S., & Tizhoosh, H. R. (2018). A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features. In S. Sundaram (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 1396-1402). Article 8628877 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2018.8628877

A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features. / Ravi, Aravind; Venugopal, Harshwin; Paul, Sruthy et al.
Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. ed. / Suresh Sundaram. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1396-1402 8628877 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ravi, A, Venugopal, H, Paul, S & Tizhoosh, HR 2018, A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features. in S Sundaram (ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018., 8628877, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1396-1402, 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bangalore, India, 11/18/18. https://doi.org/10.1109/SSCI.2018.8628877

Ravi A, Venugopal H, Paul S, Tizhoosh HR. A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features. In Sundaram S, editor, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1396-1402. 8628877. (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). doi: 10.1109/SSCI.2018.8628877

Ravi, Aravind ; Venugopal, Harshwin ; Paul, Sruthy et al. / A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. editor / Suresh Sundaram. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1396-1402 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

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abstract = "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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AB - 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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A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features

Abstract

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Keywords

ASJC Scopus subject areas

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