Difference between revisions of "Behavior recognition from visual data"
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|title=Behavior recognition from visual data | |title=Behavior recognition from visual data | ||
|image=gesturelib.jpg | |image=gesturelib.jpg | ||
| − | |description=In the literature several approaches have been used to model observed behaviors and these date back to early approaches in animal behavior analysis | + | |description=In the literature several approaches have been used to model observed behaviors and these date back to early approaches in animal behavior analysis (Baum and Eagon, 1967)(Colgan, 1978). Nowadays several techniques are used and they can be roughly classified as: State space models, Automata (e.g., Finite State Machines, Agents, etc.), Grammars (e.g., strings, T-Patterns, etc.), Bayeasian models (e.g., Hidden Markov Models), and Dynamic State Variables. The work will leverage on a huge corpus of techniques to devise the most suitable for behavior recognition from visual data. We exclude from the very beginning any deterministic approach being the phenomenon under observation complex and affected by noisy observations. The focus will be mainly of the use of dynamic graphical models (Ghahramani, 1998) and the application of bottom up learning techniques (Stolcke and Omohundro, 1993)(Stolcke and Omohundro, 1994) for model induction. |
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| + | *L. E. Baum and J. A. Eagon. An inequality with applications to statistical estimation for probabilistic functions of markov processes and to a model for ecology. Bull. Amer. Math. Soc, 73(73):360–363, 1967. | ||
| + | *P. W. Colgan. Quantitative Ethology. John Wiley & Sons, New York, 1978. | ||
| + | *A. Stolcke and S. M. Omohundro. Hidden markov model induction by bayesian model merging. In Stephen Jos é Hanson, Jack D. Cowan, and C. Lee Giles, editors, Advances in Neural Information Processing Systems, volume 5. Morgan Kaufmann, San Mateo, CA, 1993. | ||
| + | *Zoubin Ghahramani. Learning dynamic bayesian networks. Lecture Notes in Computer Science, 1387:168, 1998. | ||
| + | *A. Stolcke and S. M. Omohundro. Best-first model merging for hidden markov model induction. Technical Report TR-94-003, 1947 Center Street, Berkeley, CA, 1994. | ||
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'''''Material:''''' | '''''Material:''''' | ||
*papers from major journals and conferences | *papers from major journals and conferences | ||
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|level=Ms | |level=Ms | ||
|type=Thesis | |type=Thesis | ||
| − | |status= | + | |status=Closed |
}} | }} | ||
Latest revision as of 00:44, 22 December 2014
| Title: | Behavior recognition from visual data |  Image:gesturelib.jpg |
| Description: | In the literature several approaches have been used to model observed behaviors and these date back to early approaches in animal behavior analysis (Baum and Eagon, 1967)(Colgan, 1978). Nowadays several techniques are used and they can be roughly classified as: State space models, Automata (e.g., Finite State Machines, Agents, etc.), Grammars (e.g., strings, T-Patterns, etc.), Bayeasian models (e.g., Hidden Markov Models), and Dynamic State Variables. The work will leverage on a huge corpus of techniques to devise the most suitable for behavior recognition from visual data. We exclude from the very beginning any deterministic approach being the phenomenon under observation complex and affected by noisy observations. The focus will be mainly of the use of dynamic graphical models (Ghahramani, 1998) and the application of bottom up learning techniques (Stolcke and Omohundro, 1993)(Stolcke and Omohundro, 1994) for model induction.
Material:
Expected outcome:
Required skills or skills to be acquired:
| |
| Tutor: | MatteoMatteucci (matteo.matteucci@polimi.it), AndreaBonarini (andrea.bonarini@polimi.it) | |
| Start: | 2012/04/01 | |
| Students: | 1 - 2 | |
| CFU: | 20 - 20 | |
| Research Area: | Machine Learning | |
| Research Topic: | none | |
| Level: | Ms | |
| Type: | Thesis | |
| Status: | Closed |
