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Inter-image homographies are essential for many different tasks involving projective geometry. This paper proposes an adaptive correspondence estimation approach between person detections in a planar scene not relying on correspondence features as it is the case in many other RANSAC-based approaches. The result is a planar interimage homography calculated from estimated point correspondences. The approach is self-configurable, adaptive and provides robustness over time by exploiting temporal and geometric information. We demonstrate the manifold applicability of the proposed approach on a variety of datasets. Improved results compared to a common baseline approach are shown and the influence of error sources such as missed detections, false detections and non overlapping field of views is investigated.

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 semiautomatically 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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