NDLI: Multimodal framework based on audio-visual features for summarisation of cricket videos

Content Provider	IET Digital Library
Author	Javed, Ali Irtaza, Aun Malik, Hafiz Mahmood, Muhammad Tariq Adnan, Syed
Abstract	Sports broadcasters generate an enormous amount of video content on the cyberspace due to massive viewership all over the world. Analysis and consumption of this huge repository urges the broadcasters to apply video summarisation to extract the exciting segments from the entire video to capture user's interest and reap the storage and transmission benefits. Therefore, in this study an automatic method for key-events detection and summarisation based on audio-visual features is presented for cricket videos. Acoustic local binary pattern features are used to capture excitement level in the audio stream, which is used to train a binary support vector machine (SVM) classifier. Trained SVM classifier is used to label audio frame as an excited or non-excited frame. Excited audio frames are used to select candidate key-video frames. A decision tree-based classifier is trained to detect key-events in the input cricket videos that are then used for video summarisation. Performance of the proposed framework has been evaluated on a diverse dataset of cricket videos belonging to different tournaments and broadcasters. Experimental results indicate that the proposed method achieves an average accuracy of 95.5%, which signifies its effectiveness.
Starting Page	615
Ending Page	622
Page Count	8
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 4, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5589
Journal	IET Image Processing
Publisher Date	2019-01-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Acoustic Local Binary Pattern Features Audio Frame Audio Signal Processing Audio Stream Audio-visual Features Binary Support Vector Machine Classifier Candidate Key-video Frames Combinatorial Mathematics Computer Vision And Image Processing Technique Decision Tree Decision Tree-based Classifier Entire Video Excited Audio Frames Excitement Level Exciting Segments Feature Extraction Input Cricket Videos Key-events Detection Knowledge Engineering Technique Multimodal Framework Optical, Image And Video Signal Processing Pattern Classification Speech And Audio Signal Processing Sport Sports Broadcasters Statistics Storage Support Vector Machine Trained SVM Classifier Transmission Benefits Video Content Video Signal Processing Video Summarisation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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