2,377 research outputs found
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Optical Flow Requires Multiple Strategies (but only one network)
We show that the matching problem that underlies optical flow requires
multiple strategies, depending on the amount of image motion and other factors.
We then study the implications of this observation on training a deep neural
network for representing image patches in the context of descriptor based
optical flow. We propose a metric learning method, which selects suitable
negative samples based on the nature of the true match. This type of training
produces a network that displays multiple strategies depending on the input and
leads to state of the art results on the KITTI 2012 and KITTI 2015 optical flow
benchmarks
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