| EPSRC Reference: |
EP/E017576/1 |
| Title: |
Adaptive Models of Human Motion |
| Principal Investigator: |
Hicks, Dr YA |
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| Department: |
Sch of Engineering |
| Organisation: |
Cardiff University |
| Scheme: |
First Grant Scheme |
| Starts: |
01 April 2007 |
Ends: |
31 March 2010 |
Value (£): |
206,291
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| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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Tracking and interpreting human motion is currently one of the main active research areas in computer vision. In particular, tracking 3D articulated motion finds applications in numerous areas including sports training, computer games and computer animation. The problem of tracking 3D articulated motion is a very challenging one, considering the complexity and variety of the geometry of a human body, high level of self-occlusions present in the motion and a large number of degrees of freedom of the motion. Using models of geometry and valid poses of the human body helps to deal with these problems by imposing constraints on the interpretation of image data. Automatic motion capture systems make it possible to collect a large amount of real motion data, which can be used to automatically learn statistical models of motion. Among the popular statistical models to represent human motion are hidden Markov models (HMM). In our previous work, we developed a framework for tracking 3D articulated human motion in monocular video sequences utilising HMMs.However, currently such models tend to record averages and are static in time, so cannot easily respond to outlying individuals or changes over time in individual characteristics. We believe that to progress further the fields of modelling and tracking human motion we need to make the models adaptive to changes in the properties of motion over time. Our research will address this problem by developing machine learning algorithms to support adaptive models of human motion.The novelty of this proposal is the development of the adaptive models of human motion, which can be tuned to reflect the dynamics of motion of a particular person in a short period of time, or even to learn the dynamics of new type of motion. The adaptive models of human motion will be based on HMMs and thus will be easy to integrate within the current motion tracking algorithms relying in HMMs. I envisage that the use if the adaptive models will improve the accuracy and robustness of tracking results. As a part of this research, I will integrate the adaptive models into a framework for tracking articulated human motion and test the newly developed methods on real world video footage.However, the use of such adaptive models is not limited to tracking applications or even the computer vision area. The developed algorithms will benefit other research areas, such as speech recognition, where HMMs have been used for a long time and the adaptive models would be useful for modelling changes in speech properties over time.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.cf.ac.uk |