Modeling and estimating persistent motion with geometric flows
We propose a principled framework to model persistent motion in dynamic scenes. In contrast to previous efforts on object tracking and optical flow estimation that focus on local motion, we primarily aim at inferring a global model of persistent and collective dynamics. With this in mind, we first i...
Main Authors: | Lin, Dahua, Grimson, Eric, Fisher, John W., III |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | en_US |
Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2012
|
Online Access: | http://hdl.handle.net/1721.1/71897 https://orcid.org/0000-0003-4844-3495 https://orcid.org/0000-0002-6192-2207 |
Similar Items
-
Learning Visual Flows: A Lie Algebraic Approach
by: Lin, Dahua, et al.
Published: (2010) -
Construction of Dependent Dirichlet Processes Based on Poisson Processes
by: Lin, Dahua, et al.
Published: (2012) -
Estimation of geometric fractional Brownian motion perturbed by stochastic volatility model
by: Alhagyan, Mohammed, et al.
Published: (2015) -
Geometric motion segmentation and model selection
by: Torr, PHS
Published: (1998) -
Affine Matching with Bounded Sensor Error: A Study of Geometric Hashing and Alignment
by: Grimson W. Eric L., et al.
Published: (2004)