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

QoE-Based Low-Delay Live Streaming Using Throughput Predictions

Recently, HTTP-based adaptive streaming has become the de facto standard for video streaming over the Internet. It allows clients to dynamically adapt media characteristics to network conditions in order to ensure a high quality of experience, that is, minimize playback interruptions, while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, this task becomes particularly challenging due to the latency constraints. The challenge further increases if a client uses a wireless network, where the throughput is subject to considerable fluctuations. Consequently, live streams often exhibit latencies of up to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (Low-Latency Prediction-Based Adaptation) that is designed to operate with a transport latency of few seconds. To reach this goal, LOLYPOP leverages TCP throughput predictions on multiple time scales, from 1 to 10 seconds, along with an estimate of the prediction error distribution. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the quality of experience by maximizing the average video quality as a function of the number of skipped segments and quality transitions. In order to select an efficient prediction method, we studied the performance of several time series prediction methods in IEEE 802.11 wireless access networks. We evaluated LOLYPOP under a large set of experimental conditions limiting the transport latency to 3 seconds, against a state-of-the-art adaptation algorithm from the literature, called FESTIVE. We observed that the average video quality is by up to a factor of 3 higher than with FESTIVE. We also observed that LOLYPOP is able to reach a broader region in the quality of experience space, and thus it is better adjustable to the user profile or service provider requirements.Comment: Technical Report TKN-16-001, Telecommunication Networks Group, Technische Universitaet Berlin. This TR updated TR TKN-15-00

Anahita: A System for 3D Video Streaming with Depth Customization

Producing high-quality stereoscopic 3D content requires significantly more effort than preparing regular video footage. In order to assure good depth perception and visual comfort, 3D videos need to be carefully adjusted to specific viewing conditions before they are shown to viewers. While most stereoscopic 3D content is designed for viewing in movie theaters, where viewing conditions do not vary significantly, adapting the same content for viewing on home TV-sets, desktop displays, laptops, and mobile devices requires additional adjustments. To address this challenge, we propose a new system for 3D video streaming that provides automatic depth adjustments as one of its key features. Our system takes into account both the content and the display type in order to customize 3D videos and maximize their perceived quality. We propose a novel method for depth adjustment that is well-suited for videos of field sports such as soccer, football, and tennis. Our method is computationally efficient and it does not introduce any visual artifacts. We have implemented our 3D streaming system and conducted two user studies, which show: (i) adapting stereoscopic 3D videos for different displays is beneficial, and (ii) our proposed system can achieve up to 35% improvement in the perceived quality of the stereoscopic 3D content

Deliverable D7.5 LinkedTV Dissemination and Standardisation Report v2

This deliverable presents the LinkedTV dissemination and standardisation report for the project period of months 19 to 30 (April 2013 to March 2014)

Measuring DASH Streaming Performance from the End Users Perspective using Neubot

The popularity of DASH streaming is rapidly increasing and a number of commercial streaming services are adopting this new standard. While the benefits of building streaming services on top of the HTTP protocol are clear, further work is still necessary to evaluate and enhance the system performance from the perspective of the end user. Here we present a novel framework to evaluate the performance of rate-adaptation algorithms for DASH streaming using network measurements collected from more than a thousand Internet clients. Data, which have been made publicly available, are collected by a DASH module built on top of Neubot, an open source tool for the collection of network measurements. Some examples about the possible usage of the collected data are given, ranging from simple analysis and performance comparisons of download speeds to the performance simulation of alternative adaptation strategies using, e.g., the instantaneous available bandwidth value

Inferring Streaming Video Quality from Encrypted Traffic: Practical Models and Deployment Experience

Inferring the quality of streaming video applications is important for Internet service providers, but the fact that most video streams are encrypted makes it difficult to do so. We develop models that infer quality metrics (\ie, startup delay and resolution) for encrypted streaming video services. Our paper builds on previous work, but extends it in several ways. First, the model works in deployment settings where the video sessions and segments must be identified from a mix of traffic and the time precision of the collected traffic statistics is more coarse (\eg, due to aggregation). Second, we develop a single composite model that works for a range of different services (i.e., Netflix, YouTube, Amazon, and Twitch), as opposed to just a single service. Third, unlike many previous models, the model performs predictions at finer granularity (\eg, the precise startup delay instead of just detecting short versus long delays) allowing to draw better conclusions on the ongoing streaming quality. Fourth, we demonstrate the model is practical through a 16-month deployment in 66 homes and provide new insights about the relationships between Internet "speed" and the quality of the corresponding video streams, for a variety of services; we find that higher speeds provide only minimal improvements to startup delay and resolution

ReSEED: Social Event dEtection Dataset

Reuter T, Papadopoulos S, Mezaris V, Cimiano P. ReSEED: Social Event dEtection Dataset. In: MMSys '14. Proceedings of the 5th ACM Multimedia Systems Conference . New York: ACM; 2014: 35-40.Nowadays, digital cameras are very popular among people and quite every mobile phone has a build-in camera. Social events have a prominent role in people’s life. Thus, people take pictures of events they take part in and more and more of them upload these to well-known online photo community sites like Flickr. The number of pictures uploaded to these sites is still proliferating and there is a great interest in automatizing the process of event clustering so that every incoming (picture) document can be assigned to the corresponding event without the need of human interaction. These social events are defined as events that are planned by people, attended by people and for which the social multimedia are also captured by people. There is an urgent need to develop algorithms which are capable of grouping media by the social events they depict or are related to. In order to train, test, and evaluate such algorithms and frameworks, we present a dataset that consists of about 430,000 photos from Flickr together with the underlying ground truth consisting of about 21,000 social events. All the photos are accompanied by their textual metadata. The ground truth for the event groupings has been derived from event calendars on the Web that have been created collaboratively by people. The dataset has been used in the Social Event Detection (SED) task that was part of the MediaEval Benchmark for Multimedia Evaluation 2013. This task required participants to discover social events and organize the related media items in event-specific clusters within a collection of Web multimedia documents. In this paper we describe how the dataset has been collected and the creation of the ground truth together with a proposed evaluation methodology and a brief description of the corresponding task challenge as applied in the context of the Social Event Detection task

ESTA: An Esports Trajectory and Action Dataset

Sports, due to their global reach and impact-rich prediction tasks, are an exciting domain to deploy machine learning models. However, data from conventional sports is often unsuitable for research use due to its size, veracity, and accessibility. To address these issues, we turn to esports, a growing domain that encompasses video games played in a capacity similar to conventional sports. Since esports data is acquired through server logs rather than peripheral sensors, esports provides a unique opportunity to obtain a massive collection of clean and detailed spatiotemporal data, similar to those collected in conventional sports. To parse esports data, we develop awpy, an open-source esports game log parsing library that can extract player trajectories and actions from game logs. Using awpy, we parse 8.6m actions, 7.9m game frames, and 417k trajectories from 1,558 game logs from professional Counter-Strike tournaments to create the Esports Trajectory and Actions (ESTA) dataset. ESTA is one of the largest and most granular publicly available sports data sets to date. We use ESTA to develop benchmarks for win prediction using player-specific information. The ESTA data is available at https://github.com/pnxenopoulos/esta and awpy is made public through PyPI