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.

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.

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.