1,458 research outputs found
Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification
Although great progress in supervised person re-identification (Re-ID) has
been made recently, due to the viewpoint variation of a person, Re-ID remains a
massive visual challenge. Most existing viewpoint-based person Re-ID methods
project images from each viewpoint into separated and unrelated sub-feature
spaces. They only model the identity-level distribution inside an individual
viewpoint but ignore the underlying relationship between different viewpoints.
To address this problem, we propose a novel approach, called
\textit{Viewpoint-Aware Loss with Angular Regularization }(\textbf{VA-reID}).
Instead of one subspace for each viewpoint, our method projects the feature
from different viewpoints into a unified hypersphere and effectively models the
feature distribution on both the identity-level and the viewpoint-level. In
addition, rather than modeling different viewpoints as hard labels used for
conventional viewpoint classification, we introduce viewpoint-aware adaptive
label smoothing regularization (VALSR) that assigns the adaptive soft label to
feature representation. VALSR can effectively solve the ambiguity of the
viewpoint cluster label assignment. Extensive experiments on the Market1501 and
DukeMTMC-reID datasets demonstrated that our method outperforms the
state-of-the-art supervised Re-ID methods
Rethinking Person Re-identification from a Projection-on-Prototypes Perspective
Person Re-IDentification (Re-ID) as a retrieval task, has achieved tremendous
development over the past decade. Existing state-of-the-art methods follow an
analogous framework to first extract features from the input images and then
categorize them with a classifier. However, since there is no identity overlap
between training and testing sets, the classifier is often discarded during
inference. Only the extracted features are used for person retrieval via
distance metrics. In this paper, we rethink the role of the classifier in
person Re-ID, and advocate a new perspective to conceive the classifier as a
projection from image features to class prototypes. These prototypes are
exactly the learned parameters of the classifier. In this light, we describe
the identity of input images as similarities to all prototypes, which are then
utilized as more discriminative features to perform person Re-ID. We thereby
propose a new baseline ProNet, which innovatively reserves the function of the
classifier at the inference stage. To facilitate the learning of class
prototypes, both triplet loss and identity classification loss are applied to
features that undergo the projection by the classifier. An improved version of
ProNet++ is presented by further incorporating multi-granularity designs.
Experiments on four benchmarks demonstrate that our proposed ProNet is simple
yet effective, and significantly beats previous baselines. ProNet++ also
achieves competitive or even better results than transformer-based competitors
Vehicle Re-identification in Still Images: Application of Semi-supervised Learning and Re-ranking
Vehicle re-identification (re-ID), namely, finding exactly the same vehicle from a large number of vehicle images, remains a great challenge in computer vision. Most existing vehicle re-ID approaches follow a fully supervised learning methodology, in which sufficient labeled training data is required. However, this limits their scalability to realistic applications, due to the high cost of data labeling. In this paper, we adopted a Generative Adversarial Network (GAN) to generate unlabeled samples and enlarge the training set. A semi supervised learning scheme with the Convolutional Neural Networks (CNN) was proposed accordingly, which assigns a uniform label distribution to the unlabeled images to regularize the supervised model and improve the performance of the vehicle re-ID system. Besides, an improved re-ranking method based on the Jaccard distance and k-reciprocal nearest neighbors is proposed to optimize the initial rank list. Extensive experiments over the benchmark datasets VeR1-776, VehicleID and VehicleReID have demonstrated that the proposed method outperforms the state-of-the-art approaches for vehicle re-ID
Dual Embedding Expansion for Vehicle Re-identification
Vehicle re-identification plays a crucial role in the management of
transportation infrastructure and traffic flow. However, this is a challenging
task due to the large view-point variations in appearance, environmental and
instance-related factors. Modern systems deploy CNNs to produce unique
representations from the images of each vehicle instance. Most work focuses on
leveraging new losses and network architectures to improve the descriptiveness
of these representations. In contrast, our work concentrates on re-ranking and
embedding expansion techniques. We propose an efficient approach for combining
the outputs of multiple models at various scales while exploiting tracklet and
neighbor information, called dual embedding expansion (DEx). Additionally, a
comparative study of several common image retrieval techniques is presented in
the context of vehicle re-ID. Our system yields competitive performance in the
2020 NVIDIA AI City Challenge with promising results. We demonstrate that DEx
when combined with other re-ranking techniques, can produce an even larger gain
without any additional attribute labels or manual supervision
Person Re-identification with Deep Learning
In this work, we survey the state of the art of person re-identification and introduce the basics of the deep learning method for implementing this task. Moreover, we propose a new structure for this task.
The core content of our work is to optimize the model that is composed of a pre-trained network to distinguish images from different people with representative features. The experiment is implemented on three public person datasets and evaluated with evaluation metrics that are mean Average Precision (mAP) and Cumulative Matching Characteristic (CMC).
We take the BNNeck structure proposed by Luo et al. [25] as the baseline model. It adopts several tricks for the training, such as the mini-batch strategy of loading images, data augmentation for improving the modelâs robustness, dynamic learning rate, label-smoothing regularization, and the L2 regularization to reach a remarkable performance. Inspired from that, we propose a novel structure named SplitReID that trains the model in separated feature embedding spaces with multiple losses, which outperforms the BNNeck structure and achieves competitive performance on three datasets. Additionally, the SplitReID structure holds the property of lightweight computation complexity that it requires fewer parameters for the training and inference compared to the BNNeck structure.
Person re-identification can be deployed without high-resolution images and fixed angle of pedestrians with the deep learning method to achieve outstanding performance. Therefore, it holds an immeasurable prospect in practical applications, especially for the security fields, even though there are still some challenges like occlusions to be overcome
Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information
Vehicle re-identification (ReID) aims to find a specific vehicle identity across multiple non-overlapping cameras. The main challenge of this task is the large intra-class and small inter-class variability of vehicles appearance, sometimes related with large viewpoint variations, illumination changes or different camera resolutions. To tackle these problems, we proposed a vehicle ReID system based on ensembling deep learning features and adding different post-processing techniques. In this paper, we improve that proposal by: incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step. Additionally, we present an evaluation framework in order to evaluate CityFlow-ReID: as this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation service which is no more available; our evaluation framework allows researchers to keep on evaluating the performance of their systems in the CityFlow-ReID datasetOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Natur
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