Abstract
Local space-time features can be used to detect and characterize motion events in video. Such features are valid for recognizing motion patterns, by defining a vocabulary of primitive features, and representing each video sequence by means of a histogram, in terms of such vocabulary. In this paper, we propose a supervised vocabulary computation technique which is based on the prior classification of the training events into classes, where each class corresponds to a human action. We will compare the performance of our method with the global approach to show that not only does our method obtain better results but it is also computationally less expensive.
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Lucena, M.J., Fuertes, J.M., Pérez de la Blanca, N. (2007). Human Motion Characterization Using Spatio-temporal Features. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_11
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DOI: https://doi.org/10.1007/978-3-540-72847-4_11
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