Tracking periodic motion using Bayesian estimation

Huiyu Zhou, Andrew M. Wallace, Patrick R. Green

Research output: Contribution to journalArticlepeer-review


This paper presents a Bayesian approach to achieve efficient and accurate motion tracking in monocular image sequences. We first extract a deterministic motion model with six degrees of freedom in an on-line learning phase. This is followed by predicting the image points in successive frames, and achieving correspondence in the context of Monte Carlo estimation. Meanwhile, the motion parameters of the camera are simultaneously estimated. The experimental results show that the stable and accurate ego-motion parameters can be obtained.

Original languageEnglish
Pages (from-to)725-728
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Publication statusPublished - 2004
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: 23 Aug 200426 Aug 2004


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