2,653 research outputs found
Discriminative Feature Learning with Foreground Attention for Person Re-Identification
The performance of person re-identification (Re-ID) has been seriously
effected by the large cross-view appearance variations caused by mutual
occlusions and background clutters. Hence learning a feature representation
that can adaptively emphasize the foreground persons becomes very critical to
solve the person Re-ID problem. In this paper, we propose a simple yet
effective foreground attentive neural network (FANN) to learn a discriminative
feature representation for person Re-ID, which can adaptively enhance the
positive side of foreground and weaken the negative side of background.
Specifically, a novel foreground attentive subnetwork is designed to drive the
network's attention, in which a decoder network is used to reconstruct the
binary mask by using a novel local regression loss function, and an encoder
network is regularized by the decoder network to focus its attention on the
foreground persons. The resulting feature maps of encoder network are further
fed into the body part subnetwork and feature fusion subnetwork to learn
discriminative features. Besides, a novel symmetric triplet loss function is
introduced to supervise feature learning, in which the intra-class distance is
minimized and the inter-class distance is maximized in each triplet unit,
simultaneously. Training our FANN in a multi-task learning framework, a
discriminative feature representation can be learned to find out the matched
reference to each probe among various candidates in the gallery. Extensive
experimental results on several public benchmark datasets are evaluated, which
have shown clear improvements of our method over the state-of-the-art
approaches
PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search
Person search is a challenging problem with various real-world applications,
that aims at joint person detection and re-identification of a query person
from uncropped gallery images. Although, the previous study focuses on rich
feature information learning, it is still hard to retrieve the query person due
to the occurrence of appearance deformations and background distractors. In
this paper, we propose a novel attention-aware relation mixer (ARM) module for
person search, which exploits the global relation between different local
regions within RoI of a person and make it robust against various appearance
deformations and occlusion. The proposed ARM is composed of a relation mixer
block and a spatio-channel attention layer. The relation mixer block introduces
a spatially attended spatial mixing and a channel-wise attended channel mixing
for effectively capturing discriminative relation features within an RoI. These
discriminative relation features are further enriched by introducing a
spatio-channel attention where the foreground and background discriminability
is empowered in a joint spatio-channel space. Our ARM module is generic and it
does not rely on fine-grained supervision or topological assumptions, hence
being easily integrated into any Faster R-CNN based person search methods.
Comprehensive experiments are performed on two challenging benchmark datasets:
CUHKSYSU and PRW. Our PS-ARM achieves state-of-the-art performance on both
datasets. On the challenging PRW dataset, our PS-ARM achieves an absolute gain
of 5 in the mAP score over SeqNet, while operating at a comparable speed.Comment: Paper accepted in ACCV 202
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
STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification
In this work, we propose a novel Spatial-Temporal Attention (STA) approach to
tackle the large-scale person re-identification task in videos. Different from
the most existing methods, which simply compute representations of video clips
using frame-level aggregation (e.g. average pooling), the proposed STA adopts a
more effective way for producing robust clip-level feature representation.
Concretely, our STA fully exploits those discriminative parts of one target
person in both spatial and temporal dimensions, which results in a 2-D
attention score matrix via inter-frame regularization to measure the
importances of spatial parts across different frames. Thus, a more robust
clip-level feature representation can be generated according to a weighted sum
operation guided by the mined 2-D attention score matrix. In this way, the
challenging cases for video-based person re-identification such as pose
variation and partial occlusion can be well tackled by the STA. We conduct
extensive experiments on two large-scale benchmarks, i.e. MARS and
DukeMTMC-VideoReID. In particular, the mAP reaches 87.7% on MARS, which
significantly outperforms the state-of-the-arts with a large margin of more
than 11.6%.Comment: Accepted as a conference paper at AAAI 201
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