Non-Negative Matrix Factorization of Partial Track Data for Motion

Conference · Thu Jan 01 00:00:00 EST 2009

DOI:https://doi.org/10.1109/ICCV.2009.5459311· OSTI ID:965862

Cheriyadat, Anil M ^[1]

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1. ORNL

This paper addresses the problem of segmenting low level partial feature point tracks belonging to multiple motions. We show that the local velocity vectors at each instant of the trajectory are an effective basis for motion segmentation. We decompose the velocity profiles of point tracks into different motion components and corresponding nonnegative weights using non-negative matrix factorization (NNMF). We then segment the different motions using spectral clustering on the derived weights. We test our algorithm on the Hopkins 155 benchmarking database and several new sequences, demonstrating that the proposed algorithm can accurately segment multiple motions at a speed of a few seconds per frame. We show that our algorithm is particularly successful on low-level tracks from real-world video that are fragmented, noisy and inaccurate

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 965862
• Resource Relation: Conference: International Conference on Computer Vision, Kyoto, Japan, Japan, 20090927, 20091004
• Country of Publication: United States
• Language: English

Related Subjects

Computer Vision
Motion Segmentation
Non-negative Matrix Factorization