In this paper we propose ÖWF-OD, a new dataset for object detection in archival film content. The dataset enables the evaluation of object detection methods on image data with very different image qualities. 1,000 selected keyframes from 100 hours of video material have been annotated, 4,480 bounding boxes are labeled. In addition to these annotations, image quality measures were calculated for the keyframes in order to assess the influence of image quality on object detection. We evaluate different versions of the YOLO object detector to provide a baseline of object detection results for this dataset. The annotation data and the code to extract the keyframes are provided at https://github.com/TailoredMediaProject/OEWF_ObjectDetection.

We demonstrate a toolchain for visualising detailed road traffic data from multimodal sensors consisting of (i) the automatic, real-time extraction of movement paths of road users and noteworthy events from traffic monitoring cameras or LIDAR sensors, (ii) extraction of audio events from individual microphones and microphone arrays, (iii) a spatial data managment system storing the extracted information together with a geographic information system (GIS), and (iv) a web based viewer allowing to interactively visualise all these data in the context of a high-definition digital twin of the traffic environment. This system enables the collection of a considerable amount of objective data on road use and can be used for planning changes to traffic facilities as well as for assessing changes in the traffic environment.

Amateur film and video collections are often only sparsely documented and the contents of the material are only vaguely known. The progress in deep learning based computer vision methods over the last decade provides an opportunity to automatically generate additional descriptions that would help exploring and finding content, even if they do not meet the level of professional archive documentation. While the automatically generated metadata can facilitate interactive search and exploration, it will always need to be used with a human user in the loop, as it will not be perfect. In this paper, we describe a framework including visual content analysis tools and a web-based visualisation of the extracted metadata.

We present a C++ library implementing part 2, 3, 4 and 5 of the MPEG 7 multimedia content description standard, including the updated version finalized in 2004. The library supports handling of MPEG-7 descriptions as trees of typed objects, supporting (de)serialization from/to XML. It has convenient and powerful features such as creation of subtrees by XPath statements and is extensible at runtime. The library is available for Windows, Linux/Unix and Mac OS X. It has been provided under a free use license for several years, downloaded more than 5,200 times and used in a large number of projects. It has been published under GNU LGPL in 2009. This paper discusses the key functionalities of the library as well as some exemplary applications.

In this paper, we explore a new approach for enriching the HoG method for pedestrian detection in an unconstrained outdoor environment. The proposed algorithm is based on using gait motion since the rhythmic footprint pattern for walking people is considered the stable and characteristic feature for the detection of walking people. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirmed the robustness of our method to enhance HoG to detect walking people as well as to discriminate between single walking subject, groups of people and vehicles with a detection rate of 100%. Furthermore, the results revealed the potential of our method to be used in visual surveillance systems for identity tracking over different camera views.

On the Web, tagging is the preferred approach to describing multimedia items in order to make them searchable. The information value of tags can be significantly enhanced if they are linked to specific image regions. In this paper, we describe an approach to automatically detect regions of interest (ROIs) that are visually related to a given tag. Our technique is domain independent and works unsupervised, just by leveraging the knowledge from large-scale collections of tagged images. The ROIs are obtained by local feature matching between similarly tagged images. We demonstrate the performance and high accuracy of our approach in experiments on a set of 41 different topics and more than 9000 images.

A considerable amount of work in larger film and video restoration projects is dedicated to manually exploring the audiovisual content in order to estimate the costs for restoration and to plan the restoration. Manual exploration is a significant cost factor. In this paper we propose automatic content analysis algorithms and summarization techniques which allow the reduction of manual inspection time in a software based restoration environment. The throughput requirement for analysis of dust and other defects is reached by sparse application of the detectors in the image sequence while retaining sufficient detection accuracy. Analysis result metadata are represented in a MPEG-7 standard compliant way. The proposed defect summary visualization tools facilitate efficient exploration of visually impaired content by the user.

The GMF4iTV project (Generic Media Framework for Interactive Television) is an IST European project that developed an end-to-end broadcasting platform providing interactivity on heterogeneous multimedia devices such as Set-Top-Boxes, PCs and PDAs according to the Multimedia Home Platform (MHP) part of the DVB standard. The developed platform allows the content providers to create enhanced audiovisual contents with a degree of interactivity at moving object level or shot changes in a video. The end user is then able to interact with moving objects from the video or individual shots allowing the enjoyment of additional contents associated to them (MHP applications, HTML pages, JPEG, MPEG-4 files,...).