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CLC number: TP391

On-line Access: 2022-05-19

Received: 2020-08-16

Revision Accepted: 2022-05-19

Crosschecked: 2021-03-25

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Citations:  Bibtex RefMan EndNote GB/T7714




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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.5 P.790-800


Shot classification and replay detection for sports video summarization

Author(s):  Ali JAVED, Amen ALI KHAN

Affiliation(s):  Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan

Corresponding email(s):   ali.javed@uettaxila.edu.pk

Key Words:  Extreme learning machine, Lightweight convolutional neural network, Local octa-patterns, Shot classification, Replay detection, Video summarization

Ali JAVED, Amen ALI KHAN. Shot classification and replay detection for sports video summarization[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(5): 790-800.

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T1 - Shot classification and replay detection for sports video summarization
A1 - Ali JAVED
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J0 - Frontiers of Information Technology & Electronic Engineering
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DOI - 10.1631/FITEE.2000414

Automated analysis of sports video summarization is challenging due to variations in cameras, replay speed, illumination conditions, editing effects, game structure, genre, etc. To address these challenges, we propose an effective video summarization framework based on shot classification and replay detection for field sports videos. Accurate shot classification is mandatory to better structure the input video for further processing, i.e., key events or replay detection. Therefore, we present a lightweight convolutional neural network based method for shot classification. Then we analyze each shot for replay detection and specifically detect the successive batch of logo transition frames that identify the replay segments from the sports videos. For this purpose, we propose local octa-pattern features to represent video frames and train the extreme learning machine for classification as replay or non-replay frames. The proposed framework is robust to variations in cameras, replay speed, shot speed, illumination conditions, game structure, sports genre, broadcasters, logo designs and placement, frame transitions, and editing effects. The performance of our framework is evaluated on a dataset containing diverse YouTube sports videos of soccer, baseball, and cricket. Experimental results demonstrate that the proposed framework can reliably be used for shot classification and replay detection to summarize field sports videos.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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