CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-03-25
Cited: 0
Clicked: 5633
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.
@article{title="Shot classification and replay detection for sports video summarization",
author="Ali JAVED, Amen ALI KHAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="5",
pages="790-800",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000414"
}
%0 Journal Article
%T Shot classification and replay detection for sports video summarization
%A Ali JAVED
%A Amen ALI KHAN
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 5
%P 790-800
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000414
TY - JOUR
T1 - Shot classification and replay detection for sports video summarization
A1 - Ali JAVED
A1 - Amen ALI KHAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 5
SP - 790
EP - 800
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000414
Abstract: 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.
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