Difference between revisions of "Behavior recognition from visual data"

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Revision as of 00:34, 4 April 2012

Title: Behavior recognition from visual data
Description: [[prjDescription::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.
  • [Baum and Eagon, 1967] 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.
  • [Colgan, 1978] P. W. Colgan. Quantitative Ethology. John Wiley & Sons, New York, 1978.
  • [Stolcke and Omohundro, 1993] 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.
  • [Ghahramani, 1998] Zoubin Ghahramani. Learning dynamic bayesian networks. Lecture Notes in Computer Science, 1387:168, 1998.
  • [Stolcke and Omohundro, 1994] 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.

Material:

  • papers from major journals and conferences
  • kinet SDK for the extraction of body poses

Expected outcome:

  • general framework for the recognition of behaviors from time series
  • toolkit for behavior segmentation and recognition from time series
  • running prototype based on data coming from the Microsoft kinect sensor

Required skills or skills to be acquired:

  • understanding of techniques for behavior recognition
  • background on pattern recognition and stochastic models
  • basic understanding of computer vision
  • C++ programming under Linux or Matlab]]
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: Active
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