14,780 research outputs found
A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges
Vehicle re-identification (ReID) endeavors to associate vehicle images
collected from a distributed network of cameras spanning diverse traffic
environments. This task assumes paramount importance within the spectrum of
vehicle-centric technologies, playing a pivotal role in deploying Intelligent
Transportation Systems (ITS) and advancing smart city initiatives. Rapid
advancements in deep learning have significantly propelled the evolution of
vehicle ReID technologies in recent years. Consequently, undertaking a
comprehensive survey of methodologies centered on deep learning for vehicle
re-identification has become imperative and inescapable. This paper extensively
explores deep learning techniques applied to vehicle ReID. It outlines the
categorization of these methods, encompassing supervised and unsupervised
approaches, delves into existing research within these categories, introduces
datasets and evaluation criteria, and delineates forthcoming challenges and
potential research directions. This comprehensive assessment examines the
landscape of deep learning in vehicle ReID and establishes a foundation and
starting point for future works. It aims to serve as a complete reference by
highlighting challenges and emerging trends, fostering advancements and
applications in vehicle ReID utilizing deep learning models
Discovering Discriminative Geometric Features with Self-Supervised Attention for Vehicle Re-Identification and Beyond
In the literature of vehicle re-identification (ReID), intensive manual
labels such as landmarks, critical parts or semantic segmentation masks are
often required to improve the performance. Such extra information helps to
detect locally geometric features as a part of representation learning for
vehicles. In contrast, in this paper, we aim to address the challenge of {\em
automatically} learning to detect geometric features as landmarks {\em with no
extra labels}. To the best of our knowledge, we are the {\em first} to
successfully learn discriminative geometric features for vehicle ReID based on
self-supervised attention. Specifically, we implement an end-to-end trainable
deep network architecture consisting of three branches: (1) a global branch as
backbone for image feature extraction, (2) an attentional branch for producing
attention masks, and (3) a self-supervised branch for regularizing the
attention learning with rotated images to locate geometric features. %Our
network design naturally leads to an end-to-end multi-task joint optimization.
We conduct comprehensive experiments on three benchmark datasets for vehicle
ReID, \ie VeRi-776, CityFlow-ReID, and VehicleID, and demonstrate our
state-of-the-art performance. %of our approach with the capability of capturing
informative vehicle parts with no corresponding manual labels. We also show the
good generalization of our approach in other ReID tasks such as person ReID and
multi-target multi-camera (MTMC) vehicle tracking. {\em Our demo code is
attached in the supplementary file.
Weakly-supervised Part-Attention and Mentored Networks for Vehicle Re-Identification
Vehicle re-identification (Re-ID) aims to retrieve images with the same
vehicle ID across different cameras. Current part-level feature learning
methods typically detect vehicle parts via uniform division, outside tools, or
attention modeling. However, such part features often require expensive
additional annotations and cause sub-optimal performance in case of unreliable
part mask predictions. In this paper, we propose a weakly-supervised
Part-Attention Network (PANet) and Part-Mentored Network (PMNet) for Vehicle
Re-ID. Firstly, PANet localizes vehicle parts via part-relevant channel
recalibration and cluster-based mask generation without vehicle part
supervisory information. Secondly, PMNet leverages teacher-student guided
learning to distill vehicle part-specific features from PANet and performs
multi-scale global-part feature extraction. During inference, PMNet can
adaptively extract discriminative part features without part localization by
PANet, preventing unstable part mask predictions. We address this Re-ID issue
as a multi-task problem and adopt Homoscedastic Uncertainty to learn the
optimal weighing of ID losses. Experiments are conducted on two public
benchmarks, showing that our approach outperforms recent methods, which require
no extra annotations by an average increase of 3.0% in CMC@5 on VehicleID and
over 1.4% in mAP on VeRi776. Moreover, our method can extend to the occluded
vehicle Re-ID task and exhibits good generalization ability.Comment: This work has been submitted to the IEEE for possible publication.
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