Occluded Cloth-Changing Person Re-Identification
- URL: http://arxiv.org/abs/2403.08557v2
- Date: Fri, 15 Mar 2024 03:26:20 GMT
- Title: Occluded Cloth-Changing Person Re-Identification
- Authors: Zhihao Chen, Yiyuan Ge,
- Abstract summary: Cloth-changing person re-identification aims to retrieve pedestrians by using cloth-unrelated features in person cloth-changing scenarios.
We propose occluded cloth-changing person re-identification as a new task.
- Score: 1.7648680700685022
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cloth-changing person re-identification aims to retrieve and identify spe-cific pedestrians by using cloth-unrelated features in person cloth-changing scenarios. However, pedestrian images captured by surveillance probes usually contain occlusions in real-world scenarios. The perfor-mance of existing cloth-changing person re-identification methods is sig-nificantly degraded due to the reduction of discriminative cloth-unrelated features caused by occlusion. We define cloth-changing person re-identification in occlusion scenarios as occluded cloth-changing person re-identification (Occ-CC-ReID), and to the best of our knowledge, we are the first to propose occluded cloth-changing person re-identification as a new task. We constructed two occluded cloth-changing person re-identification datasets: Occluded-PRCC and Occluded-LTCC. The da-tasets can be obtained from the following link: https://github.com/1024AILab/Occluded-Cloth-Changing-Person-Re-Identification.
Related papers
- Keypoint Promptable Re-Identification [76.31113049256375]
Occluded Person Re-Identification (ReID) is a metric learning task that involves matching occluded individuals based on their appearance.
We introduce Keypoint Promptable ReID (KPR), a novel formulation of the ReID problem that explicitly complements the input bounding box with a set of semantic keypoints.
We release custom keypoint labels for four popular ReID benchmarks. Experiments on person retrieval, but also on pose tracking, demonstrate that our method systematically surpasses previous state-of-the-art approaches.
arXiv Detail & Related papers (2024-07-25T15:20:58Z) - Features Reconstruction Disentanglement Cloth-Changing Person Re-Identification [1.5703073293718952]
Cloth-changing person re-identification (CC-ReID) aims to retrieve specific pedestrians in a cloth-changing scenario.
Main challenge is to disentangle the clothing-related and clothing-unrelated features.
We propose features reconstruction disentanglement ReID (FRD-ReID), which can controllably decouple the clothing-unrelated and clothing-related features.
arXiv Detail & Related papers (2024-07-15T13:08:42Z) - Content and Salient Semantics Collaboration for Cloth-Changing Person Re-Identification [74.10897798660314]
Cloth-changing person Re-IDentification aims at recognizing the same person with clothing changes across non-overlapping cameras.
We propose the Content and Salient Semantics Collaboration framework, facilitating cross-parallel semantics interaction and refinement.
Our framework is simple yet effective, and the vital design is the Semantics Mining and Refinement (SMR) module.
arXiv Detail & Related papers (2024-05-26T15:17:28Z) - Identity-aware Dual-constraint Network for Cloth-Changing Person Re-identification [13.709863134725335]
Cloth-Changing Person Re-Identification (CC-ReID) aims to accurately identify the target person in more realistic surveillance scenarios, where pedestrians usually change their clothing.
Despite great progress, limited cloth-changing training samples in existing CC-ReID datasets still prevent the model from adequately learning cloth-irrelevant features.
We propose an Identity-aware Dual-constraint Network (IDNet) for the CC-ReID task.
arXiv Detail & Related papers (2024-03-13T05:46:36Z) - Clothes-Invariant Feature Learning by Causal Intervention for
Clothes-Changing Person Re-identification [118.23912884472794]
Clothes-invariant feature extraction is critical to the clothes-changing person re-identification (CC-ReID)
We argue that there exists a strong spurious correlation between clothes and human identity, that restricts the common likelihood-based ReID method P(Y|X) to extract clothes-irrelevant features.
We propose a new Causal Clothes-Invariant Learning (CCIL) method to achieve clothes-invariant feature learning.
arXiv Detail & Related papers (2023-05-10T13:48:24Z) - Identity-Guided Collaborative Learning for Cloth-Changing Person
Reidentification [29.200286257496714]
We propose a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing person ReID.
First, we design a novel clothing attention stream to reasonably reduce the interference caused by clothing information.
Second, we propose a human semantic attention and body jigsaw stream to highlight the human semantic information and simulate different poses of the same identity.
Third, a pedestrian identity enhancement stream is further proposed to enhance the identity importance and extract more favorable identity robust features.
arXiv Detail & Related papers (2023-04-10T06:05:54Z) - Unsupervised Text Deidentification [101.2219634341714]
We propose an unsupervised deidentification method that masks words that leak personally-identifying information.
Motivated by K-anonymity based privacy, we generate redactions that ensure a minimum reidentification rank.
arXiv Detail & Related papers (2022-10-20T18:54:39Z) - Identity-Sensitive Knowledge Propagation for Cloth-Changing Person
Re-identification [17.588668735411783]
Cloth-changing person re-identification (CC-ReID) aims to match person identities under clothing changes.
typical biometrics-based CC-ReID methods require cumbersome pose or body part estimators to learn cloth-irrelevant features from human biometric traits.
We propose an effective Identity-Sensitive Knowledge propagation framework (DeSKPro) for CC-ReID.
Our framework outperforms state-of-the-art methods by a large margin.
arXiv Detail & Related papers (2022-08-25T12:01:49Z) - Towards Privacy-Preserving Person Re-identification via Person Identify
Shift [19.212691296927165]
Person re-identification (ReID) requires preserving the privacy of pedestrian images used by ReID methods.
We propose a novel de-identification method designed explicitly for person ReID, named Person Identify Shift (PIS)
PIS shifts each pedestrian image from the current identity to another with a new identity, resulting in images still preserving the relative identities.
arXiv Detail & Related papers (2022-07-15T06:58:41Z) - RealGait: Gait Recognition for Person Re-Identification [79.67088297584762]
We construct a new gait dataset by extracting silhouettes from an existing video person re-identification challenge which consists of 1,404 persons walking in an unconstrained manner.
Our results suggest that recognizing people by their gait in real surveillance scenarios is feasible and the underlying gait pattern is probably the true reason why video person re-idenfification works in practice.
arXiv Detail & Related papers (2022-01-13T06:30:56Z) - Long-Term Cloth-Changing Person Re-identification [154.57752691285046]
Person re-identification (Re-ID) aims to match a target person across camera views at different locations and times.
Existing Re-ID studies focus on the short-term cloth-consistent setting, under which a person re-appears in different camera views with the same outfit.
In this work, we focus on a much more difficult yet practical setting where person matching is conducted over long-duration, e.g., over days and months.
arXiv Detail & Related papers (2020-05-26T11:27:21Z)
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.