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Abstract:
We present a novel dataset for evaluation of object matching and
recognition methods in surveillance scenarios. Dataset consists of
23,641 images, depicting 15 persons and nine vehicles. A ground truth
data -- the identity of each person or vehicle -- is provided, along
with the coordinates of the bounding box in the full camera image. The
dataset was acquired from 36 stationary camera views using a variety of
surveillance cameras with resolutions ranging from standard VGA to
three megapixel. 27 cameras observed the persons and vehicles in an
outdoor environment, while the remaining nine observed the same persons
indoors. The activity of persons was planned in advance; they drive the
cars to the parking lot, exit the cars and walk around the building,
through the main entrance, and up the stairs, towards the first floor
of the building. The intended use of the dataset is performance
evaluation of computer vision methods that aim to (re)identify people
and objects from many different viewpoints in different environments
and under variable conditions. Due to variety of camera locations,
vantage points and resolutions, the dataset provides means to adjust
the difficulty of the identification task in a controlled and
documented manner. An interface for easy integration into Matlab is
provided as well, and the data is complemented by baseline results
using the simple color histogram descriptor.
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Publication:
If you use the dataset, please cite:
Vildana Sulić Kenk, Rok Mandeljc, Stanislav Kovačič, Matej Kristan,
Melita Hajdinjak, Janez Perš: Visual
Re-Identification Across Large, Distributed Camera Networks,
Image and Vision Computing,
34(0): 11-26, February 2015. DOI:
http://dx.doi.org/10.1016/j.imavis.2014.11.002
Additional code, which comes with the above paper and makes use
of this dataset, can be found here.
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