The format agnostic production paradigm has been proposed to offer more engaging live broadcasts to the audience while ensuring the cost-efficiency of the production. An ultra-HD resolution panorama is captured, and streams for different devices and user profiles are semi-automatically generated. Information about person positions and trajectories in the video are important cues for making editing decisions for sports content. In this paper we describe a real-time person detection and tracking system for panoramic video. The approach extends our earlier tracking by detection algorithm by addressing a number of robustness issues that are especially relevant in sports content. The design of the approach is strongly driven by the requirement to process high-resolution video in real-time. We show that we can achieve improvements of the robustness of the algorithm while being able to perform real-time processing.
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