Object Recognition Based on n-gram Representation of Human Action
| Language | Japanese |
|---|---|
| Type | Proceedings of Japanese Conferences |
| Authors | Hiroshi MIKI, Atsuhiro KOJIMA, Koichi KISE |
| Title | Object Recognition Based on n-gram Representation of Human Action |
| Journal | IEICE Technical Report |
| Presentation number | PRMU2009-128 |
| Pages | pp.253-258 |
| Reviewed | Not reviewed |
| Date | November 2009 |
| Research fields | Object Recognition |
| Key | Miki2009 (id=1128) |
Abstract
In the field of object recognition, usage of human actions is applied to it, in order to tackle with the recongition of object that couldn’t be identified by only using their appearance. Most of the conventional research for object recongition from actions, however, need an intermidiate classification of action category. In our research, primitive motions of human are directly apply to object recongition without classifying action categories. First, features of human motion are extracted from video images and encoded to symbols. Then, set of n-grams is obtained from the symbol sequence and registered to corresponding object category. At recognition phases, actions toward an object are also converted into n-grams which are to be compared with that registered in object categories. In this paper, we performed experiments of object recognition using our methods and confirmed the validity of this.
Files
- …
Export this publication
Format definition
How to write the definition
Insert a field with braces like {authors}. A part enclosed in [ ... ] is omitted entirely when any field inside it is empty. Available fields:
{authors}{authors_and}{authors_ja}{authors_ja_tight}{authors_en}{authors_en_and}{authors_tight}{first_author}{title}{title_ja}{title_en}{journal}{journal_ja}{journal_en}{book_title}{volume}{number}{series}{presen_id}{pages}{pp}{num_pages}{year}{month}{month_en}{month_short}{ym}{date}{date_ja}{date_en}{publisher}{address}{location}{review}{review_ja}{review_en}{reviewed_mark}{presen_type}{type}{type_label}{doi}{url}{key}{awards}