Semantic-based traffic video retrieval using activity pattern analysis

Author

Xie, Dan ; Hu, Weiming ; Tan, Tieniu ; Peng, Junyi

Author_Institution

Inst. of Autom., Acad. Sinica, Beijing, China

Volume

1

fYear

2004

fDate

24-27 Oct. 2004

Firstpage

693

Abstract

A semantic based retrieval framework for traffic video sequences is proposed. In order to estimate the low-level motion data, a cluster tracking algorithm is developed. A novel hierarchical self-organizing map is applied to learn the activity patterns. By using activity pattern analysis and semantic concepts assignment, a set of activity models is generated, which is used as the indexing key for accessing video clips and individual vehicles in the semantic level. The proposed retrieval framework supports various queries including query by keywords, query by sketch and multiple object queries.

Keywords

image classification; image retrieval; image sequences; learning (artificial intelligence); optical tracking; parameter estimation; pattern clustering; query formulation; self-organising feature maps; video signal processing; activity models; activity pattern analysis; cluster tracking algorithm; hierarchical self-organizing map; indexing key; keywords; low-level motion data estimation; multiple object queries; semantic based retrieval; semantic-based video retrieval; sketch query; traffic video retrieval; video sequences; Clustering algorithms; Content based retrieval; Indexing; Information retrieval; Laboratories; Pattern analysis; Pattern recognition; Surveillance; Target tracking; Vehicle dynamics;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2004. ICIP '04. 2004 International Conference on

ISSN

1522-4880

Print_ISBN

0-7803-8554-3

Type

conf

DOI

10.1109/ICIP.2004.1418849

Filename

1418849