Non-Negative Matrix Factorization of Partial Track Data for Motion Segmentation

This paper addresses the problem of segmenting lowlevel 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.

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Anil M. Cheriyadat and Richard J. Radke
• Keywords: Computer Vision, ICCV 2009, Motion Segmentation, Non-Negative Matrix Factorization, Partial Track Data