Audiovisual coverage of sports events using Unmanned Aerial Vehicles (UAVs) is becoming increasingly popular. Intelligent audiovisual (A/V) shooting tools, accurately identifying the 2D region of cinematographic attention (RoCA) depicting rapidly moving target ensembles and automatically controlling the UAVs/cameras through visual content analysis, are thus needed. A novel algorithmic pipeline is proposed, implementing computational UAV cinematography for assisting sports coverage, based on semantic, human-centered visual analysis. Athlete and ball detection / tracking results as well as their spatial distribution on the image plane are the semantic features extracted from UAV video feed and exploited for RoCA extraction, based solely on present and past target detections. A PID controller visually controlling a real or virtual camera to track the RoCA and produce aesthetically pleasing shots, without exploiting 3D location-related information, is employed. The proposed method is evaluated on actual UAV footage from soccer matches and promising results are obtained.

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.

A novel method for Visual Voice Activity Detection (V-VAD) that exploits local shape and motion information appearing at spatiotemporal locations of interest for facial region video description and the Bag of Words (BoW) model for facial region video representation is proposed in this paper. Facial region video classification is subsequently performed based on Single-hidden Layer Feedforward Neural (SLFN) network trained by applying the recently proposed kernel Extreme Learning Machine (kELM) algorithm on training facial videos depicting talking and non-talking persons. Experimental results on two publicly available V-VAD data sets, denote the effectiveness of the proposed method, since better generalization performance in unseen users is achieved, compared to recently proposed state-of-the-art methods.