Discovering Clusters in Motion Time-Series Data (Preprint)
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 26, 2003
- Accession Number
- ADA461872
Entities
People
- George Kollios
- Jonathan Alon
- Stan Sclaroff
- Vladimir Pavlovic
Organizations
- Boston University