Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Action Recognition by Imprecise Hidden Markov Models Alessandro Antonucci1, Rocco de Rosa2, and Alessandro Giusti2
 

Summary: Action Recognition by Imprecise Hidden Markov Models
Alessandro Antonucci1, Rocco de Rosa2, and Alessandro Giusti2
1IDSIA, "Dalle Molle" Institute for Artificial Intelligence, Lugano, Switzerland
2Dipartimento di Scienze dell'Informazione, UniversitÓ degli Studi di Milano, Milano, Italy
Abstract-- Hidden Markov models (HMMs) are powerful
tools to capture the dynamics of a human action by providing
a sufficient level of abstraction to recognise what two video
sequences, depicting the same kind of action, have in com-
mon. If the sequence is short and hence only few data are
available, the EM algorithm, which is generally employed
to learn HMMs, might return unreliable estimates. As a
possible solution to this problem, a robust version of the EM
algorithm, which provides an interval-valued quantification
of the HMM probabilities is provided. This takes place in an
imprecise-probabilistic framework, where action recognition
can be based on the (bounds of the) likelihood assigned
by an imprecise HMM to the considered video sequence.
Experiments show that this approach is quite effective in dis-
criminating the hard-to-recognise sequences from the easy
ones. In practice, either the recognition algorithm returns a

  

Source: Antonucci, Alessandro - Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA)

 

Collections: Computer Technologies and Information Sciences