34,152 research outputs found
Learning Resolution-Invariant Deep Representations for Person Re-Identification
Person re-identification (re-ID) solves the task of matching images across
cameras and is among the research topics in vision community. Since query
images in real-world scenarios might suffer from resolution loss, how to solve
the resolution mismatch problem during person re-ID becomes a practical
problem. Instead of applying separate image super-resolution models, we propose
a novel network architecture of Resolution Adaptation and re-Identification
Network (RAIN) to solve cross-resolution person re-ID. Advancing the strategy
of adversarial learning, we aim at extracting resolution-invariant
representations for re-ID, while the proposed model is learned in an end-to-end
training fashion. Our experiments confirm that the use of our model can
recognize low-resolution query images, even if the resolution is not seen
during training. Moreover, the extension of our model for semi-supervised re-ID
further confirms the scalability of our proposed method for real-world
scenarios and applications.Comment: Accepted to AAAI 2019 (Oral
Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification
An efficient and effective person re-identification (ReID) system relieves
the users from painful and boring video watching and accelerates the process of
video analysis. Recently, with the explosive demands of practical applications,
a lot of research efforts have been dedicated to heterogeneous person
re-identification (Hetero-ReID). In this paper, we provide a comprehensive
review of state-of-the-art Hetero-ReID methods that address the challenge of
inter-modality discrepancies. According to the application scenario, we
classify the methods into four categories -- low-resolution, infrared, sketch,
and text. We begin with an introduction of ReID, and make a comparison between
Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and
compare existing datasets for performing evaluations, and survey the models
that have been widely employed in Hetero-ReID. We also summarize and compare
the representative approaches from two perspectives, i.e., the application
scenario and the learning pipeline. We conclude by a discussion of some future
research directions. Follow-up updates are avaible at:
https://github.com/lightChaserX/Awesome-Hetero-reIDComment: Accepted by IJCAI 2020. Project url:
https://github.com/lightChaserX/Awesome-Hetero-reI
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
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