Detail publikace

Visual Surveillance Metadata Management

Originální název

Visual Surveillance Metadata Management

Anglický název

Visual Surveillance Metadata Management

Jazyk

en

Originální abstrakt

The paper deals with a solution for visual surveillance metadata management. Data coming from many cameras is annotated using computer vision units to produce metadata representing moving objects in their states. It is assumed that the data is often uncertain, noisy and some states are missing. The solution consists of the following three layers: (a) data cleaning layer - improves quality of the data by smoothing it and by filling in missing states in short sequences referred to as tracks that represent a composite state of a moving object in a spatiotemporal subspace followed by one camera. (b) Data integration layer - assigns a global identity to tracks that represent the same object. (c) Persistence layer - manages the metadata in a database so that it can be used for online identification and offline querying, analyzing and mining. A Kalman filter technique is used to solve (a) and a classification based on the moving object's state and its visual properties is used in (b). An object model for layer (c) is presented too.

Anglický abstrakt

The paper deals with a solution for visual surveillance metadata management. Data coming from many cameras is annotated using computer vision units to produce metadata representing moving objects in their states. It is assumed that the data is often uncertain, noisy and some states are missing. The solution consists of the following three layers: (a) data cleaning layer - improves quality of the data by smoothing it and by filling in missing states in short sequences referred to as tracks that represent a composite state of a moving object in a spatiotemporal subspace followed by one camera. (b) Data integration layer - assigns a global identity to tracks that represent the same object. (c) Persistence layer - manages the metadata in a database so that it can be used for online identification and offline querying, analyzing and mining. A Kalman filter technique is used to solve (a) and a classification based on the moving object's state and its visual properties is used in (b). An object model for layer (c) is presented too.

BibTex


@inproceedings{BUT28830,
  author="Petr {Chmelař} and Jaroslav {Zendulka}",
  title="Visual Surveillance Metadata Management",
  annote="The paper deals with a solution for visual surveillance metadata management. Data
coming from many cameras is annotated using computer vision units to produce
metadata representing moving objects in their states. It is assumed that the data
is often uncertain, noisy and some states are missing. 

The solution consists of the following three layers: (a) data cleaning layer -
improves quality of the data by smoothing it and by filling in missing states in
short sequences referred to as tracks that represent a composite state of a
moving object in a spatiotemporal subspace followed by one camera. (b) Data
integration layer - assigns a global identity to tracks that represent the same
object. (c) Persistence layer - manages the metadata in a database so that it can
be used for online identification and offline querying, analyzing and mining. A
Kalman filter technique is used to solve (a) and a classification based on the
moving object's state and its visual properties is used in (b). An object model
for layer (c) is presented too.",
  address="IEEE Computer Society Press",
  booktitle="Eighteenth International Workshop on Database and Expert Systems Applications",
  chapter="28830",
  edition="IEEE CPS",
  howpublished="print",
  institution="IEEE Computer Society Press",
  year="2007",
  month="september",
  pages="79--83",
  publisher="IEEE Computer Society Press",
  type="conference paper"
}