Ragnar Langseth

Bagadus: An integrated real-time system for soccer analytics

The importance of winning has increased the role of performance analysis in the sports industry, and this underscores how statistics and technology keep changing the way sports are played. Thus, this is a growing area of interest, both from a computer system view in managing the technical challenges and from a sport performance view in aiding the development of athletes. In this respect, Bagadus is a real-time prototype of a sports analytics application using soccer as a case study. Bagadus integrates a sensor system, a soccer analytics annotations system, and a video processing system using a video camera array. A prototype is currently installed at Alfheim Stadium in Norway, and in this article, we describe how the system can be used in real-time to playback events. The system supports both stitched panorama video and camera switching modes and creates video summaries based on queries to the sensor system. Moreover, we evaluate the system from a systems point of view, benchmarking different approaches, algorithms, and trade-offs, and show how the system runs in real time.

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.

Soccer video and player position dataset

This paper presents a dataset of body-sensor traces and corresponding 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 20Hz 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 1Hz sampling rate. The provided 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 extraction, object tracking, background subtraction, and similar, to be tested against the ground truth contained in the sensor traces.

Tiling of panorama video for interactive virtual cameras: Overheads and potential bandwidth requirement reduction

Delivering high resolution, high bitrate panorama video to a large number of users introduces huge scaling challenges. To reduce the resource requirement, researchers have earlier proposed tiling in order to deliver different qualities in different spatial parts of the video. In our work, providing an interactive moving virtual camera to each user, tiling may be used to reduce the quality depending on the position of the virtual view. This raises new challenges compared to existing tiling approaches as the need for high quality tiles dynamically change. In this paper, we describe a tiling approach of panorama video for interactive virtual cameras where we provide initial results showing the introduced overheads and the potential reduction in bandwidth requirement.

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

High-resolution panoramic video with a wide field-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 Norwegian premier league soccer team, generates a cylindrical panorama from five 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 virtual views can be generated far below the frame-rate threshold, 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.

The Cameraman Operating My Virtual Camera is Artificial: Can the Machine Be as Good as a Humanq

In this article, we argue that the energy spent in designing autonomous camera control systems is not spent in vain. We present a real-time virtual camera system that can create smooth camera motion. Similar systems are frequently benchmarked with the human operator as the best possible reference; however, we avoid a priori assumptions in our evaluations. Our main question is simply whether we can design algorithms to steer a virtual camera that can compete with the user experience for recordings from an expert operator with several years of experience? In this respect, we present two low-complexity servoing methods that are explored in two user studies. The results from the user studies give a promising answer to the question pursued. Furthermore, all components of the system meet the real-time requirements on commodity hardware. The growing capabilities of both hardware and network in mobile devices give us hope that this system can be deployed to mobile users in the near future. Moreover, the design of the presented system takes into account that services to concurrent users must be supported.

An Evaluation of Debayering Algorithms on GPU for Real-Time Panoramic Video Recording

Modern video cameras normally only capture a single color per pixel, commonly arranged in a Bayer pattern. This means that we must restore the missing color channels in the image or the video frame in post-processing, a process referred to as debayering. In a live video scenario, this operation must be performed efficiently in order to output each frame in real-time, while also yielding acceptable visual quality. Here, we evaluate debayering algorithms implemented on a GPU for real-time panoramic video recordings using multiple 2K-resolution cameras.

Using a Commodity Hardware Video Encoder for Interactive Video Streaming

Over the last years, video streaming has become one of the most dominant Internet services. A trend now is that due to the increased availability of high-speed internet access, multimedia services are becoming more interactive and immersive. Examples of such applications are both cloud gaming and systems where users can interact with high-resolution content. Over the last few years, hardware video encoders have been built into commodity hardware. We evaluate one of these encoders in a scenario where we have individual streams delivered to the end users. Our results show that we can reduce almost half of the CPU time spent on video processing, while also greatly reducing the power consumption on the system. We also compare the visual video quality and the frame size of the hardware based encoder, and we find no significant difference compared to a software based approach.

Using a Commodity Hardware Video Encoder for Interactive Applications

Over the last years, video streaming has become one of the most dominant Internet services. Due to the increased availability of high-speed Internet access, multimedia services are becoming more interactive. Examples of such applications are both cloud gaming OnLive, 2014 and systems where users can interact with high-resolution content Gaddam et al., 2014. During the last few years, programmable hardware video encoders have been built into commodity hardware such as CPUs and GPUs. One of these encoders is evaluated in a scenario where individual streams are delivered to the end users. The results show that the visual video quality and the frame size of the hardware-based encoder are comparable to a software-based approach. To evaluate a complete system, a proposed streaming pipeline has been implemented into Quake III. It was found that running the game on a remote server and streaming the video output to a client web browser located in a typical home environment is possible and enjoyable. The interaction latency is measured to be less than 90 ms, which is below what is reported for OnLive in a similar environment

Automatic Real-Time Zooming and Panning on Salient Objects from a Panoramic Video

The proposed demo shows how our system automatically zooms and pans into tracked objects in panorama videos. At the conference site, we will set up a two-camera version of the system, generating live panorama videos, where the system zooms and pans tracking people using colored hats. Additionally, using a stored soccer game video from a five 2K camera setup at Alfheim stadium in Tromsø from the European league game between Tromsø IL and Tottenham Hotspurs, the system automatically follows the ball.