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» Tracking Discontinuous Motion Using Bayesian Inference
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HYBRID
2000
Springer
13 years 9 months ago
A Dynamic Bayesian Network Approach to Tracking Using Learned Switching Dynamic Models
Abstract. Switching linear dynamic systems (SLDS) attempt to describe a complex nonlinear dynamic system with a succession of linear models indexed by a switching variable. Unfortu...
Vladimir Pavlovic, James M. Rehg, Tat-Jen Cham
DAGM
2003
Springer
13 years 11 months ago
A Generative Model Based Approach to Motion Segmentation
We address the question of how to choose between different likelihood functions for motion estimation. To this end, we formulate motion estimation as a problem of Bayesian inferen...
Daniel Cremers, Alan L. Yuille
NIPS
2000
13 years 7 months ago
Learning Switching Linear Models of Human Motion
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. Effective models of human dynamics can be learned from motion capture data usi...
Vladimir Pavlovic, James M. Rehg, John MacCormick
ICCV
2005
IEEE
13 years 11 months ago
Bi-Directional Tracking Using Trajectory Segment Analysis
In this paper, we present a novel approach to keyframe-based tracking, called bi-directional tracking. Given two object templates in the beginning and ending keyframes, the bi-dire...
Jian Sun, Weiwei Zhang, Xiaoou Tang, Heung-Yeung S...
PCM
2004
Springer
168views Multimedia» more  PCM 2004»
13 years 11 months ago
Approximating Inference on Complex Motion Models Using Multi-model Particle Filter
Abstract. Due to its great ability of conquering clutters, which is especially useful for high-dimensional tracking problems, particle filter becomes popular in the visual trackin...
Jianyu Wang, Debin Zhao, Shiguang Shan, Wen Gao