Wearables that continuously acquire vital and other medically relevant parameters facilitate treatment optimizations for individual patients and reduce the duration of hospitalizations - thus improving the patients' quality of life. To accomplish this, we demonstrate a scalable architecture that connects wearables through a hub to the cloud, combines edge and cloud computing to provide optimal user interaction, and allows analytics on multi-stream data from those connected devices.

In this paper, we propose a way of using the Audio-Visual Description Profile (AVDP) of the MPEG-7 standard for 2D or stereo video and multichannel audio content description. Our aim is to provide means of using AVDP in such a way, that 3D video and audio content can be correctly and consistently described. Since AVDP semantics do not include ways for dealing with 3D audiovisual content, a new semantic framework within AVDP is proposed and examples of using AVDP to describe the results of analysis algorithms on stereo video and multichannel audio content are presented.

In this paper, a system for temporally consistent registration of a soccer field is proposed. It is implemented using 2D field line and arc detection as well as motion estimation for frames where too few field lines are detected. We show that a combination of these technologies yields more consistent results than common state-of-the-art approaches. As an important additional contribution, a Kalman filter is applied to suppress remaining misdetections. The system is able to register the soccer field with a mean error of 2.53 m regardless of the visible field region.

Player detection in sports video is a challenging task: In contrast to typical surveillance applications, a pan-tilt-zoom camera model is used. Therefore, simple background learning approaches cannot be used. Furthermore, camera motion causes severe motion blur, making gradient based approaches less robust than in settings where the camera is static. The contribution of this paper is a sequence adaptive approach that utilizes color information in an unsupervised manner to improve detection accuracy. Therefore, different color features, namely color histograms, color spatiograms and a color and edge directivity descriptor are evaluated. It is shown that the proposed color adaptive approach improves detection accuracy. In terms of maximum F1 score, an improvement from 0.79 to 0.81 is reached using block-wise HSV histograms. The average number of false positives per image (FPPI) at two fixed recall levels decreased by approximately 23%.

Currently, bag-of-words approaches for image categorization are very popular due to their relative simplicity, robustness and high efficiency. However, they lack the ability to represent the spatial composition of an image. This drawback has been addressed by several approaches, with spatial pyramids being the most popular. Spatial pyramids divide an image into smaller blocks, resulting in a feature vector for each block of the image. The feature vectors for these blocks are concatenated to form the feature vector of the whole image. This leads to an increase in dimension of the whole image's feature vector by a factor corresponding to the number of blocks the image is divided into. Consequently, this causes an increase in computation time proportional to the number of blocks. We propose an extension of the image feature vector by spatial features, which results in a descriptor of similar size as in the standard bag-of-words approach. The classification performance however is similar to those of spatial pyramids which use a feature vector of significantly larger size and therefore are more computationally expensive.

Video segmentation is an important task for a wide range of applications like content-based video coding or video retrieval. In this paper, a new spatio-temporal video segmentation framework is presented. It is based upon robust statistics, namely an M-estimator, and incorporates an MPEG-7 descriptor for consistent temporal labeling of identified textures. The algorithm is based on assumptions about the geometric modifications a given moving region undergoes with time as well as on its surface properties. Homogeneously moving segments are described using a parametric motion scheme. The latter is used to piecewise fit the optical flow field in order to extract rigid motion areas. Robust statistics are used to carefully constrain split, merge and contour refinement decisions. Experimental results show that regions detected by the proposed method are more reliable than the state-of-the-art. True region boundaries are moreover better detected.

Object-oriented programming is the current mainstream programming paradigm but existing RDF APIs are mostly triple-oriented. Traditional techniques for bridging a similar gap between relational databases and object-oriented programs cannot be applied directly given the different nature of Semantic Web data, for example in the semantics of class membership, inheritance relations, and object conformance to schemas.