Detection of Important Scenes in Baseball Videos via Bidirectional Time Lag Aware Deep Multiset Canonical Correlation Analysis

Abstract

A novel method for detection of important scenes in baseball videos based on correlation maximization between heterogeneous modalities via bidirectional time lag aware deep multiset canonical correlation analysis (BiTl-dMCCA) is presented in this paper. The proposed method enables detection of important scenes by collaboratively using baseball videos and their corresponding tweets. The technical contributions of this paper are twofold. First, since there are time lags between not only “tweets and corresponding multiple previous events” but also “events and corresponding multiple following posted tweets”, the proposed method considers these bidirectional time lags. Specifically, the representation of such bidirectional time lags into the derivation of their covariance matrices is newly introduced. Second, the proposed method adopts textual, visual and audio features calculated from tweets and videos as multi-modal time series features. Important scenes are detected as abnormal scenes via anomaly detection based on a generative adversarial network using multi-modal features projected by BiTl-dMCCA. The proposed method does not need any training data with annotation. Experimental results obtained by applying the proposed method to actual baseball matches show the effectiveness of the proposed method.