Incremental Learning for Motion Prediction of Pedestrians and Vehicles
Modeling and predicting human and vehicle motion is an active research domain. Owing to the difficulty in modeling the various factors that determine motion (e.g. internal state, perception) this is often tackled by applying machine learning techniques to build a statistical model, using as input a...
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| Format: | Electronic |
| Language: | English |
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Berlin, Heidelberg :
Springer Berlin Heidelberg,
2010.
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| Series: | Springer Tracts in Advanced Robotics,
64 |
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| Online Access: | https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-13642-9 |
Table of Contents:
- Part I Background
- Probabilistic Models
- Part II State of the Art
- Intentional Motion Prediction
- Hidden Markov Models
- Part III Proposed Approach
- Growing Hidden Markov Models
- Learning and Predicting Motion with GHMMs
- Part IV Experiments
- Experimental Data
- Experimental Results
- Part V Conclusion
- Conclusions and Future Work.