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Training Deformable Object Models for Human Detection Based on Alignment and Clustering
Benjamin Drayer and Thomas Brox
Department of Computer Science, Centre of Biological Signalling Studies (BIOSS), University of Freiburg, Germanydrayer@cs.uni-freiburg.de
brox@cs.uni-freiburg.de
Abstract. We propose a clustering method that considers non-rigid alignment of samples. The motivation for such a clustering is training of object detectors that consist of multiple mixture components. In particular, we consider the deformable part model (DPM) of Felzenszwalb et al., where each mixture component includes a learned deformation model. We show that alignment based clustering distributes the data better to the mixture components of the DPM than previous methods. Moreover, the alignment helps the non-convex optimization of the DPM find a consistent placement of its parts and, thus, learn more accurate part filters.
LNCS 8693, p. 406 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014