Recommended by Harald KoschOne possibility to provide mobile multimedia in domestic multimedia systems is the use of Universal Plug and Play Audio Visual (UPnP-AV) devices. In a standard UPnP-AV scenario, multimedia content provided by a Media Server device is streamed to Media Renderer devices by the initiation of a Control Point. However, there is no provisioning of context-aware multimedia content customization. This paper presents an enhancement of standard UPnP-AV services for home multimedia environments regarding context awareness. It comes up with context profile definitions, shows how this context information can be queried from the Media Renderers, and illustrates how a Control Point can use this information to tailor a media stream from the Media Server to one or more Media Renderers. Moreover, since a standard Control Point implementation only queries one Media Server at a time, there is no global view on the content of all Media Servers in the UPnP-AV network. This paper also presents an approach of multimedia content integration on the Media Server side that provides fast search for content on the network. Finally, a number of performance measurements show the overhead costs of our enhancements to UPnP-AV in order to achieve the benefits.

This paper presents a novel method for video-based traffic state detection on motorways performed on smart cameras. Camera calibration parameters are obtained from the known length of lane markings. Mean traffic speed is estimated from Kanade-Lucas-Tomasi (KLT) optical flow method using a robust outlier detection. Traffic density is estimated using a robust statistical counting method. Our method has been implemented on an embedded smart camera and evaluated under different road and illuminationconditions. It achieves a detection rate of more than 95% for stationary traffic.

Adaptation in multimedia systems is usually restricted to defensive, reactive media adaptation (often called stream-level adaptation). We argue that offensive, proactive, system-level adaptation deserves not less attention. If a distributed multimedia system cares for overall, end-to-end quality of service then it should provide a meaningful combination of both. We introduce an adaptive multimedia server (ADMS) and a supporting middleware which implement offensive adaptation based on a lean, flexible architecture. The measured costs and benefits of the offensive adaptation process are presented. We introduce an intelligent video proxy (QBIX), which implements defensive adaptation. The cost/benefit measurements of QBIX are presented elsewhere [1]. We show the benefits of the integration of QBIX in ADMS. Offensive adaptation is used to find an optimal, user-friendly configuration dynamically for ADMS, and defensive adaptation is added to take usage environment (network and terminal) constraints into account.

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We demonstrate our novel video-based real-time traffic event notification and verification system LOOK2. It generates fast and reliable traffic information about relevant traffic state and road conditions changes on observed roads. It utilizes installed road-side sensors providing low-level traffic and environmental data, as well as video sensors which gain high-level traffic information from live video analysis. Spatiotemporal data fusion is applied on all available traffic and environmental data to gain reliable traffic information. This traffic information is published by a DATEXII compliant web service to a web-based traffic desk application. Road network and traffic channel operators receive real-time and relevant traffic event notifications by using this application. The system also enables a visual verification of the notified situations.LOOK2 is a video-based traffic event notification system for road network and traffic channel operators. It generates fast and reliable traffic information about relevant changes of the traffic state and road conditions in real-time. Therefore, it makes use of installed, common road-side sensors providing low-level traffic and environmental measurement data, as well as video sensors which gain high-level traffic information from live video analysis. The live stream analysis is done either in the compressed video domain as added value to simple, installed surveillance cameras, or in the uncompressed video domain on smart cameras. Figure 1 illustrates the applied method for estimating the mean speed (Figure 1(a)) and traffic density (Figure 1(b)) for individual lanes in the uncompressed video domain. (a) Motion vector extraction (b) Occupancy computationFigure 1. Traffic speed and density estimation. For the compressed video domain, a feature-based traffic state estimation method is applied. The method performs statistical computations on motion vectors and applies supervised learning to estimate the prevailing traffic state. The gained high-level traffic states are spatio-temporally fused with all available low-level measurements of installed sensors on the roads. The fusion results are then published by a DATEX II compliant service to a web-based traffic desk application. With this application, traffic operators and editors are notified about relevant traffic state and road condition changes on the monitored roads in real-time. A direct relation of published events with available traffic cameras enables for an instant event verification (Figure 2).Figure 2. Real-time traffic state verification with camera live streams.The LOOK2 system has been developed together with ASFINAG -the Austrian operator of motorways and expressways -and has been tested by traffic editors in the production environment for several months.ACKNOWLEDGMENT

At the Industrial Surveillance Day, ASFINAG and the Alpen Adria Universitt Klagenfurt (in particular the Institute of Information Technology and the Institute of Networked and Embedded Systems) demonstrate a show case of their video-based level of service (LOS) classification for smart cameras. This LOS classification system has been developed in a joint Lakeside Labs project in Klagenfurt, Austria. It is part of a case study which aims at improving the quality of traffic messages for the two particular traffic situations level-of-service (LOS) and weather-related road conditions (WRRC) on two dedicated test tracks on Austrian motorways. Using a live connection to a smart camera at one of these test tracks, we plan to show a live demonstration for visual speed estimation and LOS classification. This demo is coordinated with our partner SLR Engineering, which provided the smart cameras for the case study.

This paper discusses scaling issues of a mobile multimedia tour guide. Making tourist-information available in a substantially large geographical area (e.g. a federal state in Austria) raises new questions, compared to providing similar information in a limited area (such as a museum). First, we have to assume a heterogeneous network infrastructure containing high and low bandwidth links and even total network loss. Video streaming is therefore not possible at any place. Secondly, the total amount of data grows linearly to the number of Points of Interest (POIs) which are augmented by the tour guide. Therefore, a preloading of all data onto a device with limited storage is not possible. A possible solution to these problems is hoarding, i.e. preloading an "appropriate" subset of data. The crucial question is to find the proper subset in dependence of the actual context. The paper discusses the questions of (1) what kind of context information should be considered and (2) what kind of usage patterns can be assumed. Based on these considerations hoarding strategies are developed for the tour guide. The strategies are finally evaluated with real-world data from a federal state wide tourist-card system.