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Typically, archival performance videos are: filmed in a single shot, lengthy, affected by camera operation, and originate from various video formats. To be useful, a video of a whole performance needs to be segmented into discrete acts that represent individual clips within the total performance; however, this is not a simple task due to the characteristics of the video content.

The Circus Oz video collection is an existing performance video archive that comprises over 1,074 videos totaling over 1,000 hours of viewing. To deliver their video collection to users, a prototype of the Circus Oz performance video archive system has been developed which includes system architecture and database schema.

For the purpose of video segmentation, we identify the specific clues that indicate where a performance video is likely to be segmented: that is when an applause sound is detected in combination with one or more other clues such as black frames and image changes.

An applause detection technique for multiple applause classes has been proposed. In order to evaluate the performance of the proposed technique, an audio data set together with applause ground truth data on a sample of the Circus Oz performance videos have been developed. This applause data set contains three applause classes: less clap, more clap, and pure clap.

The proposed applause detection technique uses both characteristic-based and classification- based approaches. Our experiments show that minimum applause strength and duration values are the two essential threshold values for improving the precision of applause detection using the classification-based approach. In this approach, we found the optimum combination of several audio features. In our applause classification experiment, we achieved 83%, 94%, and 100% correctly classified for quaternary, ternary, and binary class classification respectively.

Using the clues we identified, we proposed a method for detecting end-of-act using applause sound detection, black frames detection and image comparison. The experiment shows that the precision and recall of the end-of-act-detection method is 49% and 92% respectively, making the task of manual annotators much more productive. [author summary]

The segmentation into acts of a circus performance video is challenging as the content has similar characteristics to other performance videos but is quite different from movies, TV programs, and home videos. Segmentation is useful as a long duration circus show usually contains several shorter segments that are acts. We propose a new method for detecting end-of-act within circus performance videos. Unlike other temporal video segmentation methods, this method does not rely on shot detection techniques and uses audio and video content analysis separately. First is audio content analysis, for detecting applause on the circus audio stream. Second is image analysis. The applause is further analyzed to test whether this applause occurs at the end-of-act. An end-of-act is detected, if the image(s) before and after the applause are different or there are black frames just after the applause. Otherwise, it is not the end-of-act. The experiment to detect end-of-act on Circus Oz performance videos achieved a 92.27 % recall and 49.05 % precision, providing useful clues that assist human annotators to segment circus video into acts. [authors summary]

Designing a ‘living archive’ that will enable new forms of circus performance to be realised is a complex and dynamic challenge. This paper discusses the methods and approaches used by the research team in the design of the Circus Oz Living archive. Essential to this project has been the design of a responsive methodology that could embrace the diverse areas of knowledge and practice that have led to a design outcome that integrates the affordances of the circus with those of digital technologies. The term ‘living archive’ has been adopted as a means to articulate the dynamic nature of the archive. This is an archive that will always be evolving, not only because of the on going collection of content, but more importantly
because the performance of the archive users will themselves become part of the archive collection. [authors summary]