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Person tracking in videos is crucial in different areas such as security applications. In this work we present a method that first finds human presence probabilities on discrete locations via variational Bayesian inference using images obtained from omnidirectional cameras and then uses that information to solve the tracking problem as a flow optimization problem. In our experiments on the BOMNI dataset,...
In this study, we investigate the techniques to automatically predict the extraversion trait among the Big Five personality traits (Openness to Experience, Conscientiousness, Extraversion, Agreableness, Emotional Stability) using visual nonverbal cues in small group meetings. Our aim is to increase the prediction accuracy by transferring the knowledge that can be extracted from conversational video...
In this study, we investigate the techniques to automatically predict the extraversion trait among the Big Five personality traits (Openness to Experience, Conscientiousness, Extraversion, Agreableness, Emotional Stability) using visual nonverbal cues in small group meetings. Our aim is to increase the prediction accuracy by transferring the knowledge that can be extracted from conversational video...
This paper proposes a novel algorithm to perform hand shape classification using depth sensors, without relying on color or temporal information. Hence, the system is independent of lighting conditions and does not need a hand registration step. The proposed method uses randomized classification forests (RDF) to assign class labels to each pixel on a depth image, and the final class label is determined...
Omnidirectional cameras have a lot of potential for surveillance and ambient intelligence applications, since they provide increased coverage with fewer cameras. We introduce the new BOMNI dataset, collected with two omnidirectional cameras simultaneously. The dataset contains single subject and multi-subject interaction scenarios, as well as actions relevant for ambient assisted living, such as falling...
Polyphonic music transcription is a fundamental problem in computer music and over the last decade many sophisticated and application-specific methods have been proposed for its solution. However, most techniques cannot make fully use of all the available training data efficiently and do not scale well beyond a certain size. In this study, we develop an approach based on matrix factorization that...
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