513,773 research outputs found
Occluded Person Re-identification
Person re-identification (re-id) suffers from a serious occlusion problem
when applied to crowded public places. In this paper, we propose to retrieve a
full-body person image by using a person image with occlusions. This differs
significantly from the conventional person re-id problem where it is assumed
that person images are detected without any occlusion. We thus call this new
problem the occluded person re-identitification. To address this new problem,
we propose a novel Attention Framework of Person Body (AFPB) based on deep
learning, consisting of 1) an Occlusion Simulator (OS) which automatically
generates artificial occlusions for full-body person images, and 2) multi-task
losses that force the neural network not only to discriminate a person's
identity but also to determine whether a sample is from the occluded data
distribution or the full-body data distribution. Experiments on a new occluded
person re-id dataset and three existing benchmarks modified to include
full-body person images and occluded person images show the superiority of the
proposed method.Comment: 6 pages, 7 figures, IEEE International Conference of Multimedia and
Expo 201
PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification
Person re-identification is the problem of recognizing people across
different images or videos with non-overlapping views. Although there has been
much progress in person re-identification over the last decade, it remains a
challenging task because appearances of people can seem extremely different
across diverse camera viewpoints and person poses. In this paper, we propose a
novel framework for person re-identification by analyzing camera viewpoints and
person poses in a so-called Pose-aware Multi-shot Matching (PaMM), which
robustly estimates people's poses and efficiently conducts multi-shot matching
based on pose information. Experimental results using public person
re-identification datasets show that the proposed methods outperform
state-of-the-art methods and are promising for person re-identification from
diverse viewpoints and pose variances.Comment: 12 pages, 12 figures, 4 table
Illumination-Adaptive Person Re-identification
Most person re-identification (ReID) approaches assume that person images are
captured under relatively similar illumination conditions. In reality,
long-term person retrieval is common, and person images are often captured
under different illumination conditions at different times across a day. In
this situation, the performances of existing ReID models often degrade
dramatically. This paper addresses the ReID problem with illumination
variations and names it as {\em Illumination-Adaptive Person Re-identification
(IA-ReID)}. We propose an Illumination-Identity Disentanglement (IID) network
to dispel different scales of illuminations away while preserving individuals'
identity information. To demonstrate the illumination issue and to evaluate our
model, we construct two large-scale simulated datasets with a wide range of
illumination variations. Experimental results on the simulated datasets and
real-world images demonstrate the effectiveness of the proposed framework.Comment: Accepted by TM
Person Re-identification Using Visual Attention
Despite recent attempts for solving the person re-identification problem, it
remains a challenging task since a person's appearance can vary significantly
when large variations in view angle, human pose, and illumination are involved.
In this paper, we propose a novel approach based on using a gradient-based
attention mechanism in deep convolution neural network for solving the person
re-identification problem. Our model learns to focus selectively on parts of
the input image for which the networks' output is most sensitive to and
processes them with high resolution while perceiving the surrounding image in
low resolution. Extensive comparative evaluations demonstrate that the proposed
method outperforms state-of-the-art approaches on the challenging CUHK01,
CUHK03, and Market 1501 datasets.Comment: Published at IEEE International Conference on Image Processing 201
Robust Depth-based Person Re-identification
Person re-identification (re-id) aims to match people across non-overlapping
camera views. So far the RGB-based appearance is widely used in most existing
works. However, when people appeared in extreme illumination or changed
clothes, the RGB appearance-based re-id methods tended to fail. To overcome
this problem, we propose to exploit depth information to provide more invariant
body shape and skeleton information regardless of illumination and color
change. More specifically, we exploit depth voxel covariance descriptor and
further propose a locally rotation invariant depth shape descriptor called
Eigen-depth feature to describe pedestrian body shape. We prove that the
distance between any two covariance matrices on the Riemannian manifold is
equivalent to the Euclidean distance between the corresponding Eigen-depth
features. Furthermore, we propose a kernelized implicit feature transfer scheme
to estimate Eigen-depth feature implicitly from RGB image when depth
information is not available. We find that combining the estimated depth
features with RGB-based appearance features can sometimes help to better reduce
visual ambiguities of appearance features caused by illumination and similar
clothes. The effectiveness of our models was validated on publicly available
depth pedestrian datasets as compared to related methods for person
re-identification.Comment: IEEE Transactions on Image Processing Early Acces
Adversarial Open-World Person Re-Identification
In a typical real-world application of re-id, a watch-list (gallery set) of a
handful of target people (e.g. suspects) to track around a large volume of
non-target people are demanded across camera views, and this is called the
open-world person re-id. Different from conventional (closed-world) person
re-id, a large portion of probe samples are not from target people in the
open-world setting. And, it always happens that a non-target person would look
similar to a target one and therefore would seriously challenge a re-id system.
In this work, we introduce a deep open-world group-based person re-id model
based on adversarial learning to alleviate the attack problem caused by similar
non-target people. The main idea is learning to attack feature extractor on the
target people by using GAN to generate very target-like images (imposters), and
in the meantime the model will make the feature extractor learn to tolerate the
attack by discriminative learning so as to realize group-based verification.
The framework we proposed is called the adversarial open-world person
re-identification, and this is realized by our Adversarial PersonNet (APN) that
jointly learns a generator, a person discriminator, a target discriminator and
a feature extractor, where the feature extractor and target discriminator share
the same weights so as to makes the feature extractor learn to tolerate the
attack by imposters for better group-based verification. While open-world
person re-id is challenging, we show for the first time that the
adversarial-based approach helps stabilize person re-id system under imposter
attack more effectively.Comment: 17 pages, 3 figures, Accepted by European Conference on Computer
Vision 201
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
Orientation Driven Bag of Appearances for Person Re-identification
Person re-identification (re-id) consists of associating individual across
camera network, which is valuable for intelligent video surveillance and has
drawn wide attention. Although person re-identification research is making
progress, it still faces some challenges such as varying poses, illumination
and viewpoints. For feature representation in re-identification, existing works
usually use low-level descriptors which do not take full advantage of body
structure information, resulting in low representation ability.
%discrimination. To solve this problem, this paper proposes the mid-level
body-structure based feature representation (BSFR) which introduces body
structure pyramid for codebook learning and feature pooling in the vertical
direction of human body. Besides, varying viewpoints in the horizontal
direction of human body usually causes the data missing problem, , the
appearances obtained in different orientations of the identical person could
vary significantly. To address this problem, the orientation driven bag of
appearances (ODBoA) is proposed to utilize person orientation information
extracted by orientation estimation technic. To properly evaluate the proposed
approach, we introduce a new re-identification dataset (Market-1203) based on
the Market-1501 dataset and propose a new re-identification dataset (PKU-Reid).
Both datasets contain multiple images captured in different body orientations
for each person. Experimental results on three public datasets and two proposed
datasets demonstrate the superiority of the proposed approach, indicating the
effectiveness of body structure and orientation information for improving
re-identification performance.Comment: 13 pages, 15 figures, 3 tables, submitted to IEEE Transactions on
Circuits and Systems for Video Technolog
Multi-Channel Pyramid Person Matching Network for Person Re-Identification
In this work, we present a Multi-Channel deep convolutional Pyramid Person
Matching Network (MC-PPMN) based on the combination of the semantic-components
and the color-texture distributions to address the problem of person
re-identification. In particular, we learn separate deep representations for
semantic-components and color-texture distributions from two person images and
then employ pyramid person matching network (PPMN) to obtain correspondence
representations. These correspondence representations are fused to perform the
re-identification task. Further, the proposed framework is optimized via a
unified end-to-end deep learning scheme. Extensive experiments on several
benchmark datasets demonstrate the effectiveness of our approach against the
state-of-the-art literature, especially on the rank-1 recognition rate.Comment: 9 pages, 5 figures, 7 tables and accepted by the 32nd AAAI Conference
on Artificial Intelligenc
Distance-based Camera Network Topology Inference for Person Re-identification
In this paper, we propose a novel distance-based camera network topology
inference method for efficient person re-identification. To this end, we first
calibrate each camera and estimate relative scales between cameras. Using the
calibration results of multiple cameras, we calculate the speed of each person
and infer the distance between cameras to generate distance-based camera
network topology. The proposed distance-based topology can be applied
adaptively to each person according to its speed and handle diverse transition
time of people between non-overlapping cameras. To validate the proposed
method, we tested the proposed method using an open person re-identification
dataset and compared to state-of-the-art methods. The experimental results show
that the proposed method is effective for person re-identification in the
large-scale camera network with various people transition time.Comment: 10 pages, 11 figure
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