Computational UAV Cinematography for Intelligent Shooting Based on Semantic Visual Analysis

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.