Abstract
There has been a recent interest in segmenting action sequences into
meaningful parts (action primitives) and to model actions on a
higher level based on these action primitives. Unlike previous works where action primitives are defined
a-priori and search is made for them later, we present a sequential and statistical
learning algorithm for
automatic detection of the action primitives and the action grammar
based on these primitives. We model a set of actions using a
single HMM whose structure is learned incrementally as we observe
new types. Actions are modeled with sufficient number of Gaussians which would
become the states of an HMM for an action. For different actions we
find the states that are common in the actions which are then treated
as an action primitive.
meaningful parts (action primitives) and to model actions on a
higher level based on these action primitives. Unlike previous works where action primitives are defined
a-priori and search is made for them later, we present a sequential and statistical
learning algorithm for
automatic detection of the action primitives and the action grammar
based on these primitives. We model a set of actions using a
single HMM whose structure is learned incrementally as we observe
new types. Actions are modeled with sufficient number of Gaussians which would
become the states of an HMM for an action. For different actions we
find the states that are common in the actions which are then treated
as an action primitive.
| Original language | English |
|---|---|
| Book series | Lecture Notes in Computer Science |
| Pages (from-to) | 31-40 |
| Number of pages | 10 |
| ISSN | 0302-9743 |
| DOIs | |
| Publication status | Published - 2009 |
| Event | SCIA 2009 - Oslo, Norway Duration: 15 Jun 2009 → 18 Jun 2009 Conference number: 16 |
Conference
| Conference | SCIA 2009 |
|---|---|
| Number | 16 |
| Country/Territory | Norway |
| City | Oslo |
| Period | 15/06/2009 → 18/06/2009 |