Accession Number:

ADA461872

Title:

Discovering Clusters in Motion Time-Series Data (Preprint)

Descriptive Note:

Conference paper

Corporate Author:

BOSTON UNIV MA DEPT OF COMPUTER SCIENCE

Report Date:

2003-03-26

Pagination or Media Count:

8.0

Abstract:

A new approach is proposed for clustering time-series data. The approach can be used to discover groupings of similar object motions that were observed in a video collection. A finite mixture of hidden Markov models HMMs is fitted to the motion data using the expectation-maximization EM framework. Previous approaches for HMM-based clustering employ a k-means formulation, where each sequence is assigned to only a single HMM. In contrast, the formulation presented in this paper allows each sequence to belong to more than a single HMM with some probability, and the hard decision about the sequence class membership can be deferred until a later time when such a decision is required. Experiments with simulated data demonstrate the benefit of using this EM-based approach when there is more overlap in the processes generating the data. Experiments with real data show the promising potential of HMM-based motion clustering in a number of applications.

Subject Categories:

  • Operations Research
  • Cybernetics
  • Statistics and Probability
  • Recording and Playback Devices

Distribution Statement:

APPROVED FOR PUBLIC RELEASE