Resource-constrained re-identification in camera networks
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In multi-camera surveillance, association of people detected in different camera views over
time, known as person re-identification, is a fundamental task. Re-identification is a challenging
problem because of changes in the appearance of people under varying camera conditions. Existing
approaches focus on improving the re-identification accuracy, while no specific effort has
yet been put into efficiently utilising the available resources that are normally limited in a camera
network, such as storage, computation and communication capabilities. In this thesis, we aim to
perform and improve the task of re-identification under constrained resources. More specifically,
we reduce the data needed to represent the appearance of an object through a proposed feature
selection method and a difference-vector representation method.
The proposed feature-selection method considers the computational cost of feature extraction
and the cost of storing the feature descriptor jointly with the feature’s re-identification performance
to select the most cost-effective and well-performing features. This selection allows us
to improve inter-camera re-identification while reducing storage and computation requirements
within each camera. The selected features are ranked in the order of effectiveness, which enable
a further reduction by dropping the least effective features when application constraints require
this conformity. We also reduce the communication overhead in the camera network by transferring
only a difference vector, obtained from the extracted features of an object and the reference
features within a camera, as an object representation for the association.
In order to reduce the number of possible matches per association, we group the objects appearing
within a defined time-interval in un-calibrated camera pairs. Such a grouping improves
the re-identification, since only those objects that appear within the same time-interval in a camera
pair are needed to be associated. For temporal alignment of cameras, we exploit differences
between the frame numbers of the detected objects in a camera pair. Finally, in contrast to
pairwise camera associations used in literature, we propose a many-to-one camera association
method for re-identification, where multiple cameras can be candidates for having generated the
previous detections of an object. We obtain camera-invariant matching scores from the scores
obtained using the pairwise re-identification approaches. These scores measure the chances of a
correct match between the objects detected in a group of cameras.
Experimental results on publicly available and in-lab multi-camera image and video datasets
show that the proposed methods successfully reduce storage, computation and communication
requirements while improving the re-identification rate compared to existing re-identification
approaches.
Authors
Tahir, Syed FahadCollections
- Theses [3702]