1,168 research outputs found
Vehicle Re-identification Using Quadruple Directional Deep Learning Features
In order to resist the adverse effect of viewpoint variations for improving
vehicle re-identification performance, we design quadruple directional deep
learning networks to extract quadruple directional deep learning features
(QD-DLF) of vehicle images. The quadruple directional deep learning networks
are with similar overall architecture, including the same basic deep learning
architecture but different directional feature pooling layers. Specifically,
the same basic deep learning architecture is a shortly and densely connected
convolutional neural network to extract basic feature maps of an input square
vehicle image in the first stage. Then, the quadruple directional deep learning
networks utilize different directional pooling layers, i.e., horizontal average
pooling (HAP) layer, vertical average pooling (VAP) layer, diagonal average
pooling (DAP) layer and anti-diagonal average pooling (AAP) layer, to compress
the basic feature maps into horizontal, vertical, diagonal and anti-diagonal
directional feature maps, respectively.
Finally, these directional feature maps are spatially normalized and
concatenated together as a quadruple directional deep learning feature for
vehicle re-identification. Extensive experiments on both VeRi and VehicleID
databases show that the proposed QD-DLF approach outperforms multiple
state-of-the-art vehicle re-identification methods.Comment: this paper has been submitted to IEEE Transactions on Intelligent
Transportation Systems, under revie
Vehicle Re-Identification Based on Complementary Features
In this work, we present our solution to the vehicle re-identification
(vehicle Re-ID) track in AI City Challenge 2020 (AIC2020). The purpose of
vehicle Re-ID is to retrieve the same vehicle appeared across multiple cameras,
and it could make a great contribution to the Intelligent Traffic System(ITS)
and smart city. Due to the vehicle's orientation, lighting and inter-class
similarity, it is difficult to achieve robust and discriminative representation
feature. For the vehicle Re-ID track in AIC2020, our method is to fuse features
extracted from different networks in order to take advantages of these networks
and achieve complementary features. For each single model, several methods such
as multi-loss, filter grafting, semi-supervised are used to increase the
representation ability as better as possible. Top performance in City-Scale
Multi-Camera Vehicle Re-Identification demonstrated the advantage of our
methods, and we got 5-th place in the vehicle Re-ID track of AIC2020. The codes
are available at https://github.com/gggcy/AIC2020_ReID
Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
Existing vehicle re-identification methods commonly use spatial pooling
operations to aggregate feature maps extracted via off-the-shelf backbone
networks. They ignore exploring the spatial significance of feature maps,
eventually degrading the vehicle re-identification performance. In this paper,
firstly, an innovative spatial graph network (SGN) is proposed to elaborately
explore the spatial significance of feature maps. The SGN stacks multiple
spatial graphs (SGs). Each SG assigns feature map's elements as nodes and
utilizes spatial neighborhood relationships to determine edges among nodes.
During the SGN's propagation, each node and its spatial neighbors on an SG are
aggregated to the next SG. On the next SG, each aggregated node is re-weighted
with a learnable parameter to find the significance at the corresponding
location. Secondly, a novel pyramidal graph network (PGN) is designed to
comprehensively explore the spatial significance of feature maps at multiple
scales. The PGN organizes multiple SGNs in a pyramidal manner and makes each
SGN handles feature maps of a specific scale. Finally, a hybrid pyramidal graph
network (HPGN) is developed by embedding the PGN behind a ResNet-50 based
backbone network. Extensive experiments on three large scale vehicle databases
(i.e., VeRi776, VehicleID, and VeRi-Wild) demonstrate that the proposed HPGN is
superior to state-of-the-art vehicle re-identification approaches
DCDLearn: Multi-order Deep Cross-distance Learning for Vehicle Re-Identification
Vehicle re-identification (Re-ID) has become a popular research topic owing
to its practicability in intelligent transportation systems. Vehicle Re-ID
suffers the numerous challenges caused by drastic variation in illumination,
occlusions, background, resolutions, viewing angles, and so on. To address it,
this paper formulates a multi-order deep cross-distance learning
(\textbf{DCDLearn}) model for vehicle re-identification, where an efficient
one-view CycleGAN model is developed to alleviate exhaustive and enumerative
cross-camera matching problem in previous works and smooth the domain
discrepancy of cross cameras. Specially, we treat the transferred images and
the reconstructed images generated by one-view CycleGAN as multi-order
augmented data for deep cross-distance learning, where the cross distances of
multi-order image set with distinct identities are learned by optimizing an
objective function with multi-order augmented triplet loss and center loss to
achieve the camera-invariance and identity-consistency. Extensive experiments
on three vehicle Re-ID datasets demonstrate that the proposed method achieves
significant improvement over the state-of-the-arts, especially for the small
scale dataset
Stripe-based and Attribute-aware Network: A Two-Branch Deep Model for Vehicle Re-identification
Vehicle re-identification (Re-ID) has been attracting increasing interest in
the field of computer vision due to the growing utilization of surveillance
cameras in public security. However, vehicle Re-ID still suffers a similarity
challenge despite the efforts made to solve this problem. This challenge
involves distinguishing different instances with nearly identical appearances.
In this paper, we propose a novel two-branch stripe-based and attribute-aware
deep convolutional neural network (SAN) to learn the efficient feature
embedding for vehicle Re-ID task. The two-branch neural network, consisting of
stripe-based branch and attribute-aware branches, can adaptively extract the
discriminative features from the visual appearance of vehicles. A horizontal
average pooling and dimension-reduced convolutional layers are inserted into
the stripe-based branch to achieve part-level features. Meanwhile, the
attribute-aware branch extracts the global feature under the supervision of
vehicle attribute labels to separate the similar vehicle identities with
different attribute annotations. Finally, the part-level and global features
are concatenated together to form the final descriptor of the input image for
vehicle Re-ID. The final descriptor not only can separate vehicles with
different attributes but also distinguish vehicle identities with the same
attributes. The extensive experiments on both VehicleID and VeRi databases show
that the proposed SAN method outperforms other state-of-the-art vehicle Re-ID
approaches
Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
This paper introduces our solution for the Track2 in AI City Challenge 2020
(AICITY20). The Track2 is a vehicle re-identification (ReID) task with both the
real-world data and synthetic data. Our solution is based on a strong baseline
with bag of tricks (BoT-BS) proposed in person ReID. At first, we propose a
multi-domain learning method to joint the real-world and synthetic data to
train the model. Then, we propose the Identity Mining method to automatically
generate pseudo labels for a part of the testing data, which is better than the
k-means clustering. The tracklet-level re-ranking strategy with weighted
features is also used to post-process the results. Finally, with multiple-model
ensemble, our method achieves 0.7322 in the mAP score which yields third place
in the competition. The codes are available at
https://github.com/heshuting555/AICITY2020_DMT_VehicleReID.Comment: Solution for AI City Challenge, CVPR2020 Workshop. Codes are at
https://github.com/heshuting555/AICITY2020_DMT_VehicleReI
Eliminating cross-camera bias for vehicle re-identification
Vehicle re-identification (reID) often requires recognize a target vehicle in
large datasets captured from multi-cameras. It plays an important role in the
automatic analysis of the increasing urban surveillance videos, which has
become a hot topic in recent years. However, the appearance of vehicle images
is easily affected by the environment that various illuminations, different
backgrounds and viewpoints, which leads to the large bias between different
cameras. To address this problem, this paper proposes a cross-camera adaptation
framework (CCA), which smooths the bias by exploiting the common space between
cameras for all samples. CCA first transfers images from multi-cameras into one
camera to reduce the impact of the illumination and resolution, which generates
the samples with the similar distribution. Then, to eliminate the influence of
background and focus on the valuable parts, we propose an attention alignment
network (AANet) to learn powerful features for vehicle reID. Specially, in
AANet, the spatial transfer network with attention module is introduced to
locate a series of the most discriminative regions with high-attention weights
and suppress the background. Moreover, comprehensive experimental results have
demonstrated that our proposed CCA can achieve excellent performances on
benchmark datasets VehicleID and VeRi-776
DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio
Vehicle Re-identification (ReID) is an important yet challenging problem in
computer vision. Compared to other visual objects like faces and persons,
vehicles simultaneously exhibit much larger intraclass viewpoint variations and
interclass visual similarities, making most exiting loss functions designed for
face recognition and person ReID unsuitable for vehicle ReID. To obtain a
high-performance vehicle ReID model, we present a novel Distance Shrinking with
Angular Marginalizing (DSAM) loss function to perform hybrid learning in both
the Original Feature Space (OFS) and the Feature Angular Space (FAS) using the
local verification and the global identification information. Specifically, it
shrinks the distance between samples of the same class locally in the Original
Feature Space while keeps samples of different classes far away in the Feature
Angular Space. The shrinking and marginalizing operations are performed during
each iteration of the training process and are suitable for different SoftMax
based loss functions. We evaluate the DSAM loss function on three large vehicle
ReID datasets with detailed analyses and extensive comparisons with many
competing vehicle ReID methods. Experimental results show that our DSAM loss
enhances the SoftMax loss by a large margin on the PKU-VD1-Large dataset:
10.41% for mAP, 5.29% for cmc1, and 4.60% for cmc5. Moreover, the mAP is
increased by 9.34% on the PKU-VehicleID dataset and 8.73% on the VeRi-776
dataset. Source code will be released to facilitate further studies in this
research direction
Attribute-guided Feature Learning Network for Vehicle Re-identification
Vehicle re-identification (reID) plays an important role in the automatic
analysis of the increasing urban surveillance videos, which has become a hot
topic in recent years. However, it poses the critical but challenging problem
that is caused by various viewpoints of vehicles, diversified illuminations and
complicated environments. Till now, most existing vehicle reID approaches focus
on learning metrics or ensemble to derive better representation, which are only
take identity labels of vehicle into consideration. However, the attributes of
vehicle that contain detailed descriptions are beneficial for training reID
model. Hence, this paper proposes a novel Attribute-Guided Network (AGNet),
which could learn global representation with the abundant attribute features in
an end-to-end manner. Specially, an attribute-guided module is proposed in
AGNet to generate the attribute mask which could inversely guide to select
discriminative features for category classification. Besides that, in our
proposed AGNet, an attribute-based label smoothing (ALS) loss is presented to
better train the reID model, which can strength the distinct ability of vehicle
reID model to regularize AGNet model according to the attributes. Comprehensive
experimental results clearly demonstrate that our method achieves excellent
performance on both VehicleID dataset and VeRi-776 dataset.Comment: arXiv admin note: text overlap with arXiv:1912.1019
Looking GLAMORous: Vehicle Re-Id in Heterogeneous Cameras Networks with Global and Local Attention
Vehicle re-identification (re-id) is a fundamental problem for modern
surveillance camera networks. Existing approaches for vehicle re-id utilize
global features and local features for re-id by combining multiple subnetworks
and losses. In this paper, we propose GLAMOR, or Global and Local Attention
MOdules for Re-id. GLAMOR performs global and local feature extraction
simultaneously in a unified model to achieve state-of-the-art performance in
vehicle re-id across a variety of adversarial conditions and datasets (mAPs
80.34, 76.48, 77.15 on VeRi-776, VRIC, and VeRi-Wild, respectively). GLAMOR
introduces several contributions: a better backbone construction method that
outperforms recent approaches, group and layer normalization to address
conflicting loss targets for re-id, a novel global attention module for global
feature extraction, and a novel local attention module for self-guided
part-based local feature extraction that does not require supervision.
Additionally, GLAMOR is a compact and fast model that is 10x smaller while
delivering 25% better performance
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