Multimodal framework based on audio‐visual features for summarisation of cricket videos. Issue 4 (6th March 2019)

• Record Type: Journal Article
• Authors: Javed, Ali; Irtaza, Aun; Malik, Hafiz; Mahmood, Muhammad Tariq; Adnan, Syed
• Is Part Of: IET image processing. Volume 13:Issue 4(2019)
• Page Start: 615
• Page End: 622
• Publication Date: 2019-03-06
• Subjects: support vector machines -- pattern classification -- audio signal processing -- sport -- feature extraction -- decision trees -- video signal processing; multimodal framework -- audio‐visual features -- sports broadcasters -- video content -- video summarisation -- exciting segments -- entire video -- storage -- transmission benefits -- key‐events detection -- acoustic local binary pattern features -- excitement level -- audio stream -- binary support vector machine classifier -- trained SVM classifier -- audio frame -- excited audio frames -- candidate key‐video frames -- decision tree‐based classifier -- input cricket videos; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5589
• Languages: English
• ISSNs: 1751-9659

Abstract

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.