2017-01-03T15:17:17 n5374

The multi-camera surveillance database: for the task of person re-identification

Viewed: 600

This multi-camera surveillance dataset, the SAIVT-SoftBio database, was captured from an existing surveillance network, to enable the evaluation of person recognition and re-identification models in a reallife multi-camera surveillance environment.

The dataset consists of 150 people moving through a building environment, recorded by eight surveillance cameras. Each camera captures data at 25 frames per second, at a resolution of 704 x 576 pixels, and is calibrated using Tsai’s method. The placement of cameras is a real-life surveillance setup, and cameras have been placed to provide maximal coverage of the space (with some overlap) and observation of the entrances to the building. The dataset was collected in an uncontrolled manner, so subjects can travel any route through the building. Thus, the vast majority of subjects will only pass through a subset of the camera network and that subset varies from person to person. This provides a highly unconstrained environment in which to test person re-identification models.

The frames are recorded from when the subject enters the building through one of the three main doorways visible in Camera 4, Camera 7 and Camera 5/8, until they leave observation either through exiting the building or entering a lecture theatre. Any frames which are significantly occluded, have been omitted.

XML files are used to store information about the database to enable different evaluations to be easily performed based on which subset of the dataset fits the desired criteria. For each subject, an XML file is used to summarise the camera views
and frame information which can be used to select subjects which fit the desired evaluation conditions (e.g. only subjects that exist in specific cameras or locations can be selected).

The overall dataset is also summarised in an XML file, which provides information on the camera calibration data for each subject.

Access rights

In addition to citing our paper, we kindly request that the following text be included in an acknowledgements section at the end of your publications: We would like to thank the SAIVT Research Labs at Queensland University of Technology (QUT) for freely supplying us with the SAIVT-SoftBio database for our research.

Geographical area of data collection

kmlPolyCoords
153.025013,-27.476409

Publications

Bialkowski, Alina, Denman, Simon, Lucey, Patrick, Sridharan, Sridha, & Fookes, Clinton B. (2012) A database for person re-identification in multi-camera surveillance networks. In Digital Image Computing : Techniques and Applications (DICTA 2012), 3-5 December 2012, Esplanade Hotel, Fremantle, WA, available at http://dx.doi.org/10.1109/DICTA.2012.6411689

Research areas

Multi-camera
Information and computing sciences
Artifical intelligence and image processing
Electrical and electronic engineering
Unconstrained
Softbio
Engineering
Signal processing
Soft biometrics
Surveillance

Cite this collection

Bialkowski, Alina, Denman, Simon, Lucey, Patrick, Sridharan, Sridha, & Fookes, Clinton B. (2012) A database for person re-identification in multi-camera surveillance networks. In Digital Image Computing : Techniques and Applications (DICTA 2012), 3-5 December 2012, Esplanade Hotel, Fremantle, WA, available at http://eprints.qut.edu.au/53437/
Bialkowski ,Alina; Fookes,Clinton; Denman,Simon; Sridharan ,Sridha. (2014): The multi-camera surveillance database: for the task of person re-identification. [Queensland University of Technology]. http://dx.doi.org/10.4225/09/586b33b1aa774

Licence


Creative Commons Attribution-Share Alike 4.0 (CC-BY-SA)
http://creativecommons.org/licenses/by-sa/4.0/

Copyright

Queensland University of Technology, 2012

Dates of data collection

From 2012-01-01 to 2013-12-31

Connections

Was collected by
Alina Bialkowski   (Researcher)
Clinton Fookes  (Researcher)
Simon Denman  (Researcher)
Sridha Sridharan  (Researcher)

Contacts

Name: Alina Bialkowski

Other

Data type:
Individually identifiable
Date record created:
2014-06-27T10:35:15
Date record modified:
2017-01-03T15:17:17
Record status:
Published - Open Access