Multimodal framework based on audio‐visual features for summarisation of cricket videos

TLDR

Sports broadcasters produce vast amounts of cricket video content worldwide, prompting the need for summarisation to highlight exciting segments and reduce storage and transmission costs. This study presents an automatic key‑event detection and summarisation method for cricket videos that relies on audio‑visual features. The approach extracts acoustic local binary pattern features to train a binary SVM that labels audio frames as excited or not, uses excited frames to select candidate key‑video frames, and then trains a decision‑tree classifier to detect key events for summarisation, evaluated on a diverse cricket video dataset. Experimental results show the method achieves an average accuracy of 95.5 %, demonstrating its effectiveness.

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.