961 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
VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification
One fundamental challenge of vehicle re-identification (re-id) is to learn
robust and discriminative visual representation, given the significant
intra-class vehicle variations across different camera views. As the existing
vehicle datasets are limited in terms of training images and viewpoints, we
propose to build a unique large-scale vehicle dataset (called VehicleNet) by
harnessing four public vehicle datasets, and design a simple yet effective
two-stage progressive approach to learning more robust visual representation
from VehicleNet. The first stage of our approach is to learn the generic
representation for all domains (i.e., source vehicle datasets) by training with
the conventional classification loss. This stage relaxes the full alignment
between the training and testing domains, as it is agnostic to the target
vehicle domain. The second stage is to fine-tune the trained model purely based
on the target vehicle set, by minimizing the distribution discrepancy between
our VehicleNet and any target domain. We discuss our proposed multi-source
dataset VehicleNet and evaluate the effectiveness of the two-stage progressive
representation learning through extensive experiments. We achieve the
state-of-art accuracy of 86.07% mAP on the private test set of AICity
Challenge, and competitive results on two other public vehicle re-id datasets,
i.e., VeRi-776 and VehicleID. We hope this new VehicleNet dataset and the
learned robust representations can pave the way for vehicle re-id in the
real-world environments
Keypoint-Aligned Embeddings for Image Retrieval and Re-identification
Learning embeddings that are invariant to the pose of the object is crucial
in visual image retrieval and re-identification. The existing approaches for
person, vehicle, or animal re-identification tasks suffer from high intra-class
variance due to deformable shapes and different camera viewpoints. To overcome
this limitation, we propose to align the image embedding with a predefined
order of the keypoints. The proposed keypoint aligned embeddings model
(KAE-Net) learns part-level features via multi-task learning which is guided by
keypoint locations. More specifically, KAE-Net extracts channels from a feature
map activated by a specific keypoint through learning the auxiliary task of
heatmap reconstruction for this keypoint. The KAE-Net is compact, generic and
conceptually simple. It achieves state of the art performance on the benchmark
datasets of CUB-200-2011, Cars196 and VeRi-776 for retrieval and
re-identification tasks.Comment: 8 pages, 7 figures, accepted to WACV 202
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