Andreas Aristidou

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This work studies the problem of violence detection in audio data, which can be used for automated content rating. We employ some popular frame-level audio features both from the time and frequency domain. Afterwards, several statistics of the calculated feature sequences are fed as input to a Support Vector Machine classifier, which decides about the(More)
Optical motion capture systems suffer from marker occlusions resulting in loss of useful information. This paper addresses the problem of real-time joint localisation of legged skeletons in the presence of such missing data. The data is assumed to be labelled 3d marker positions from a motion capture system. An integrated framework is presented which(More)
Motion capture techniques are becoming a popular method for digitizing folk dances for preservation and dissemination. Although technically the captured data can be of very high quality, folk dancing, in contrast to choreographed performances, allow for stylistic variations and improvisations that cannot be easily captured by the data themselves. The(More)
Exergames do not have the capacity to detect whether the players are really enjoying the game-play. The games are not intelligent enough to detect significant emotional states and adapt accordingly in order to offer a better user experience for the players. We propose a set of body motion features, based on the Effort component of Laban Movement Analysis(More)
Recently, there has been an increasing use of pre-recorded motion capture data, making motion indexing and classification essential for animating virtual characters and synthesising different actions. In this paper, we use a variety of features that encode characteristics of motion using the Body, Effort, Shape and Space components of Laban Movement(More)
Motion capture (mocap) technology is an efficient method for digitizing art-performances, and it is becoming a popular method for the preservation and dissemination of dances. However, stylistic variations of human motion are difficult to measure and cannot be directly extracted from the motion capture data itself. In this work, we present a framework based(More)