Be your own cameraman: real-time support for zooming and panning into stored and live panoramic video

International audienceHigh-resolution panoramic video with a wide eld-of-view is popular in many contexts. However, in many examples, like surveillance and sports, it is often desirable to zoom and pan into the generated video. A challenge in this respect is real-time support, but in this demo, we present an end-to- end real-time panorama system with interactive zoom and panning. Our system installed at Alfheim stadium, a Nor- wegian premier league soccer team, generates a cylindrical panorama from ve 2K cameras live where the perspective is corrected in real-time when presented to the client. This gives a better and more natural zoom compared to existing systems using perspective panoramas and zoom operations using plain crop. Our experimental results indicate that vir- tual views can be generated far below the frame-rate thresh- old, i.e., on a GPU, the processing requirement per frame is about 10 milliseconds. The proposed demo lets participants interactively zoom and pan into stored panorama videos generated at Alfheim stadium and from a live 2-camera array on-site

Real-Time HDR Panorama Video

The interest for wide field of view panorama video is in-creasing. In this respect, we have an application that uses an array of cameras that overlook a soccer stadium. The input of these cameras are stitched together to provide a panoramic view of the stadium. One of the challenges we face is that large parts of the field are obscured by shad-ows on sunny days. Such circumstances cause unsatisfying video quality. We have therefore implemented and evaluated multiple algorithms related to high dynamic range (HDR) video. The evaluation shows that a combination of several approaches gives the most useful results in our scenario

Soccer video and player position dataset

This paper presents a dataset of body-sensor traces and cor-responding videos from several professional soccer games captured in late 2013 at the Alfheim Stadium in Tromsø, Norway. Player data, including field position, heading, and speed are sampled at 20 Hz using the highly accurate ZXY Sport Tracking system. Additional per-player statistics, like total distance covered and distance covered in different speed classes, are also included with a 1 Hz sampling rate. The pro-vided videos are in high-definition and captured using two stationary camera arrays positioned at an elevated position above the tribune area close to the center of the field. The camera array is configured to cover the entire soccer field, and each camera can be used individually or as a stitched panorama video. This combination of body-sensor data and videos enables computer-vision algorithms for feature ex-traction, object tracking, background subtraction, and sim-ilar, to be tested against the ground truth contained in the sensor traces

Interactive zoom and panning from live Panoramic video

Panorama video is becoming increasingly popular, and we present an end-to-end real-time system to interactively zoom and pan into high-resolution panoramic videos. Compared to existing systems using perspective panoramas with cropping, our approach creates a cylindrical panorama. Here, the perspective is corrected in real-time, and the result is a better and more natural zoom. Our experimental results also indicate that such zoomed virtual views can be generated far below the frame-rate threshold. Taking into account recent trends in device development, our approach should be able to scale to a large number of concurrent users in the near future

Efficient Implementation and Processing of a Real-Time Panorama Video Pipeline

Abstract—High resolution, wide field of view video generated from multiple camera feeds has many use cases. However, processing the different steps of a panorama video pipeline in real-time is challenging due to the high data rates and the stringent requirements of timeliness. We use panorama video in a sport analysis system where video events must be generated in real-time. In this respect, we present a system for real-time panorama video generation from an array of low-cost CCD HD video cameras. We describe how we have implemented different components and evaluated alternatives. We also present performance results with and without co-processors like graphics processing units (GPUs), and we evaluate each individual component and show how the entire pipeline is able to run in real-time on commodity hardware. I