Towards Precise Intra-camera Supervised Person Re-identification
- URL: http://arxiv.org/abs/2002.04932v2
- Date: Fri, 11 Dec 2020 07:14:23 GMT
- Title: Towards Precise Intra-camera Supervised Person Re-identification
- Authors: Menglin Wang, Baisheng Lai, Haokun Chen, Jianqiang Huang, Xiaojin
Gong, Xian-Sheng Hua
- Abstract summary: Intra-camera supervision (ICS) for person re-identification (Re-ID) assumes that identity labels are independently annotated within each camera view.
Lack of inter-camera labels makes the ICS Re-ID problem much more challenging than the fully supervised counterpart.
Our approach performs even comparable to state-of-the-art fully supervised methods in two of the datasets.
- Score: 54.86892428155225
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Intra-camera supervision (ICS) for person re-identification (Re-ID) assumes
that identity labels are independently annotated within each camera view and no
inter-camera identity association is labeled. It is a new setting proposed
recently to reduce the burden of annotation while expect to maintain desirable
Re-ID performance. However, the lack of inter-camera labels makes the ICS Re-ID
problem much more challenging than the fully supervised counterpart. By
investigating the characteristics of ICS, this paper proposes camera-specific
non-parametric classifiers, together with a hybrid mining quintuplet loss, to
perform intra-camera learning. Then, an inter-camera learning module consisting
of a graph-based ID association step and a Re-ID model updating step is
conducted. Extensive experiments on three large-scale Re-ID datasets show that
our approach outperforms all existing ICS works by a great margin. Our approach
performs even comparable to state-of-the-art fully supervised methods in two of
the datasets.
Related papers
- CLIP-based Camera-Agnostic Feature Learning for Intra-camera Person Re-Identification [11.882424627567998]
We propose a novel framework called CLIP-based Camera-Agnostic Feature Learning (CCAFL) for ICS ReID.
Two custom modules are designed to guide the model to actively learn camera-agnostic pedestrian features.
In experiments on popular ReID datasets, we arrive at 58.9% in terms of mAP accuracy, surpassing state-of-the-art methods by 7.6%.
arXiv Detail & Related papers (2024-09-29T05:43:01Z) - View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network [87.36616083812058]
view-decoupled transformer (VDT) is proposed as a simple yet effective framework for aerial-ground person re-identification.
Two major components are designed in VDT to decouple view-related and view-unrelated features.
In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images.
arXiv Detail & Related papers (2024-03-21T16:08:21Z) - Pseudo Labels Refinement with Intra-camera Similarity for Unsupervised
Person Re-identification [8.779246907359706]
Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels.
Most clustering-based methods roughly divide image features into clusters and neglect the feature distribution noise caused by domain shifts among different cameras.
We propose a novel label refinement framework with clustering intra-camera similarity.
arXiv Detail & Related papers (2023-04-25T08:04:12Z) - Handling Label Uncertainty for Camera Incremental Person
Re-Identification [17.5026399908583]
Incremental learning for person re-identification (ReID) aims to develop models that can be trained with a continuous data stream.
New data collected from new cameras may probably contain an unknown proportion of identities seen before.
We propose a novel framework ExtendOVA to handle the class overlap issue.
arXiv Detail & Related papers (2022-10-17T02:59:54Z) - Cross-Camera Feature Prediction for Intra-Camera Supervised Person
Re-identification across Distant Scenes [70.30052164401178]
Person re-identification (Re-ID) aims to match person images across non-overlapping camera views.
ICS-DS Re-ID uses cross-camera unpaired data with intra-camera identity labels for training.
Cross-camera feature prediction method to mine cross-camera self supervision information.
Joint learning of global-level and local-level features forms a global-local cross-camera feature prediction scheme.
arXiv Detail & Related papers (2021-07-29T11:27:50Z) - Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for
Unsupervised Person Re-Identification [60.36551512902312]
unsupervised person re-identification (re-ID) aims to learn discriminative models with unlabeled data.
One popular method is to obtain pseudo-label by clustering and use them to optimize the model.
In this paper, we propose a unified framework to solve both problems.
arXiv Detail & Related papers (2021-03-08T09:13:06Z) - Camera-aware Proxies for Unsupervised Person Re-Identification [60.26031011794513]
This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations.
We propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera.
Based on the camera-aware proxies, we design both intra- and inter-camera contrastive learning components for our Re-ID model.
arXiv Detail & Related papers (2020-12-19T12:37:04Z) - Intra-Camera Supervised Person Re-Identification [87.88852321309433]
We propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation.
This eliminates the most time-consuming and tedious inter-camera identity labelling process.
We formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method for Intra-Camera Supervised (ICS) person re-id.
arXiv Detail & Related papers (2020-02-12T15:26:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.