Replay and key-events detection for sports video summarization using confined elliptical local ternary patterns and extreme learning machine

Sports broadcasters generate enormous amount of video content viewed all over the world. To capture the user interests in the rebroadcasted content, the sports videos are summarized that need the manual inspection and analysis. However, the huge repository and long duration of videos make manual analysis and summarization a laborious and time-consuming job. To overcome this problem, efforts have been made for automatic video summarization. In this paper, a novel framework to summarize sports videos is presented. It has been observed that the replays within a sports video represent key-events and these events can be used for video summarization. It has been noted that replays are usually sandwiched between start and stop of gradual-transitions. A thresholding-based approach is used to identify gradual transition effect (i.e. fade-in, fade-out) in sports video. The Gaussian mixture model (GMM) is then applied to key-event candidates to extract silhouettes and generate motion history image (MHI) for each key-event. The MHIs are processed using Confined Elliptical Local Ternary Patterns (CE-LTPs) for feature extraction. Extreme learning machine (ELM) classifier is used to learn the underlying model for events. A trained ELM-based classifier is then used for key-event detection. The output of classifier is then used for key-event labeling, replay detection, and complete game summarization. Performance of the proposed framework is evaluated on a dataset consisting of 20 videos of four different sports. Experimental results indicate the effectiveness of the proposed framework in terms of replays and key-events detection from selected dataset.