You buy a new TV and get a new remote, you buy a DVD player and you get another remote, you buy a sound system and guess what& Almost every digital media product comes with its own designated remote control (RC). What would happen if one remote adapts to the owner's activities and is able to have cross-device control? In this paper we describe a design exploration conducted to create new ways to interact with digital media products. The main ingredients are: fit for a social setting, portable, and including physical interaction. The result is an exploration of a button less single-hand remote control concept.

A large number of videos are captured and shared by the audience from musical concerts. However, such recordings are typically perceived as boring mainly because of their limited view, poor visual quality and incomplete coverage. It is our objective to enrich the viewing experience of these recordings by exploiting the abundance of content from multiple sources. In this paper, we propose a novel \Virtual Director system that automatically combines the most desirable segments from different recordings resulting in a single video stream, called mashup. We start by eliciting requirements from focus groups, interviewing professional video editors and consulting film grammar literature. We design a formal model for automatic mashup generation based on maximizing the degree of fulfillment of the requirements. Various audio-visual content analysis techniques are used to determine how well the requirements are satisfied by a recording. To validate the system, we compare our mashups with two other mashups: manually created by a professional video editor and machine generated by random segment selection. The mashups are evaluated in terms of visual quality, content diversity and pleasantness by 40 subjects. The results show that our mashups and the manual mashups are perceived as comparable, while both of them are significantly higher than the random mashups in all three terms.

Digital video capturing is getting popular with the decreasing price of camcorders and the increasing availability of devices with embedded video cameras such as digital-still cameras, mobile phones and PDAs. While a raw home video is considered as visually non-appealing, having multiple recordings of the same event provides the opportunity to combine audio and video segments from different cameras for improving quality and aesthetics. Mixing content from different recordings requires precise synchronization among the recordings. In most present applications, synchronization is done manually and considered as a very tedious task. In this paper, we propose a novel automated synchronization approach based on detecting and matching audio and video features extracted from the recorded content. We assess experimentally three realizations of this approach on a common data set and make recommendations on the usability of the different realizations in practical use cases. The realizations have no limitations on the number and movement of the cameras. Moreover, they are robust against various ambient noises and audio-visual artifacts occurring during the recordings.

Distinguishing children from adults in digital images is beneficial for enabling ambient intelligent applications. For example, when an ambient intelligent shop window detects a child, it could adapt its content to the particular limitations and needs of children. This article presents a method to automatically detect children in digital images. The method is based on the fact that the size of a person's iris is practically constant after birth while the head grows as the person grows adult. Therefore adults can be distinguished from children based on the face/iris size ratio. The faces are detected using a standard Viola-Jones face detection technique. The irises are found and measured by using iterative Canny edge detection and a modified circular Hough transform. Our results show an accuracy of over 80% when tested on a set of 289 real life photographs of frontal faces.

An increasing number of people regularly capture video in social occasions like weddings, parties and holiday trips. As a result, multiple video recordings are made from a single event providing different view angles and wider coverage. This gives an opportunity to produce a desired video summary from the event, combining the videos with the most favorable views from multiple recordings. In order to mix contents from different cameras, the recordings require very precise synchronization in time. This task is very tedious and presently done manually. We present two methods to synchronize multiple videos based on the identical audio content present in the recordings. The first method utilizes audio-classification and the synchronization between two recordings is determined by correlating the audio classes. The second method uses audio-fingerprints to represent the recorded audio. The synchronization is determined by fingerprint matches between the different recordings. The experimental results show that the audio-classification method requires recordings, at least a couple of minutes long, with large temporal overlap to determine the synchronization point. The method using audio-fingerprints requires at least 3 second long overlapping audio and resulted inperfect synchronization in all the examined cases.

Automatic systems are needed for audiovisual databases to efficiently index, browse, summarize and retrieve, because the amount of stored data is increasing tremendously. Historically film production techniques, have developed, in part, to convey e.g. meaning or atmosphere to the viewer. By studying these techniques, established guidelines for conveying meaning may be incorporated into automated tools for video analysis. In the current paper we present an approach in this area to classify different shot types, such as long shots, medium shots and close ups, which are important elements of video production. Based on a set of features calculated from the audiovisual content (e.g. presence of camera motion and size of detected faces), a Bayesian classifier distinguishes between six different shot types. The performance of this novel generic field of view classifier in terms of precision and recall is promising

Combining audiovisual sequences from different cameras requires precise alignment in time. Current synchronization techniques involve using geometrical properties such as camera positions and object features. In this paper, we present a synchronization method based on detecting flashes present in the video content. Such flashes, generated by still cameras, cause a sharp bright frame in the videos. They are detected using an adaptive threshold on luminance variation across the frames. The resulting flash sequences are searched for matches. The matching flashes indicate overlapping content, and allow determining the offset time between cameras. The experimental results from flash detection and flash sequence matching show perfect synchronization in all examined cases.