Enhanced model selection for motion segmentation

Authors:
L. Zappella;X. Lladó;J. Salvi
Affiliations:
Institute of Informatics and Applications, University of Girona, Girona, Spain;Institute of Informatics and Applications, University of Girona, Girona, Spain;Institute of Informatics and Applications, University of Girona, Girona, Spain
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation.