Abstract-The visual voice activity detection (V-VAD) problem in unconstrained environments is investigated in this paper. A novel method for V-VAD in the wild, exploiting local shape and motion information appearing at spatiotemporal locations of interest for facial video segment description and the bag of words model for facial video segment representation, is proposed. Facial video segment classification is subsequently performed using the state-of-the-art classification algorithms. Experimental results on one publicly available V-VAD dataset denote the effectiveness of the proposed method, since it achieves better generalization performance in unseen users, when compared to the recently proposed state-of-the-art methods. Additional results on a new unconstrained dataset provide evidence that the proposed method can be effective even in such cases in which any other existing method fails. Index Terms-Action recognition, bag of words model, kernel extreme learning machine, space-time interest points, voice activity detection in the wild.
REF exploited non deep models by using local shape and motion information appearing at spatiotemporal locations of interest for facial video segment and a SVM classifier at the end on unconstrained movie data.
Visual Voice Activity Detection in the Wild
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Computer Science