Abstract
Recognizing activities in image sequences is an open problem in computer vision. In this paper we present a method to extract the most significant frames from an activity sequence. We name these frames as the keyframes. Moreover, we describe a pre-processing stage in order to build a robust representation for different human movements. Using this representation, we build an activity eigenspace that is used to obtain a probability measure. We use this measure to develop a method to select the activity keyframes automatically.
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© 2000 Springer-Verlag Berlin Heidelberg
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Varona, X., Gonzàlez, J., Roca, F.X., Villanueva, J.J. (2000). Automatic Selection of Keyframes for Activity Recognition. In: Nagel, HH., Perales López, F.J. (eds) Articulated Motion and Deformable Objects. AMDO 2000. Lecture Notes in Computer Science, vol 1899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10722604_15
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DOI: https://doi.org/10.1007/10722604_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67912-7
Online ISBN: 978-3-540-44591-3
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