A generative model for music transcription
IEEE Transactions on Audio, Speech, and Language Processing
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This paper's intention is to present a new approach for decomposing motion trajectories. The proposed algorithm is based on non-negative matrix factorization, which is applied to a grid like representation of the trajectories. From a set of training samples a number of basis primitives is generated. These basis primitives are applied to reconstruct an observed trajectory. The reconstruction information can be used afterwards for classification. An extension of the reconstruction approach furthermore enables to predict the observed movement into the future. The proposed algorithm goes beyond the standard methods for tracking, since it does not use an explicit motion model but is able to adapt to the observed situation. In experiments we used real movement data to evaluate several aspects of the proposed approach.