A Benchmark of Video-Based Clothes-Changing Person Re-Identification
- URL: http://arxiv.org/abs/2211.11165v1
- Date: Mon, 21 Nov 2022 03:38:18 GMT
- Title: A Benchmark of Video-Based Clothes-Changing Person Re-Identification
- Authors: Likai Wang, Xiangqun Zhang, Ruize Han, Jialin Yang, Xiaoyu Li, Wei
Feng, Song Wang
- Abstract summary: We study the relatively new yet practical problem of clothes-changing video-based person re-identification (CCVReID)
We develop a two-branch confidence-aware re-ranking framework for handling the CCVReID problem.
We build two new benchmark datasets for CCVReID problem.
- Score: 20.010401795892125
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Person re-identification (Re-ID) is a classical computer vision task and has
achieved great progress so far. Recently, long-term Re-ID with clothes-changing
has attracted increasing attention. However, existing methods mainly focus on
image-based setting, where richer temporal information is overlooked. In this
paper, we focus on the relatively new yet practical problem of clothes-changing
video-based person re-identification (CCVReID), which is less studied. We
systematically study this problem by simultaneously considering the challenge
of the clothes inconsistency issue and the temporal information contained in
the video sequence for the person Re-ID problem. Based on this, we develop a
two-branch confidence-aware re-ranking framework for handling the CCVReID
problem. The proposed framework integrates two branches that consider both the
classical appearance features and cloth-free gait features through a
confidence-guided re-ranking strategy. This method provides the baseline method
for further studies. Also, we build two new benchmark datasets for CCVReID
problem, including a large-scale synthetic video dataset and a real-world one,
both containing human sequences with various clothing changes. We will release
the benchmark and code in this work to the public.
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) - 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) - CCPA: Long-term Person Re-Identification via Contrastive Clothing and
Pose Augmentation [2.1756081703276]
Long-term Person Re-Identification aims at matching an individual across cameras after a long period of time.
We propose CCPA: Contrastive Clothing and Pose Augmentation framework for LRe-ID.
arXiv Detail & Related papers (2024-02-22T11:16:34Z) - Semantic-aware Consistency Network for Cloth-changing Person
Re-Identification [8.885551377703944]
We present a Semantic-aware Consistency Network (SCNet) to learn identity-related semantic features.
We generate the black-clothing image by erasing pixels in the clothing area.
We further design a semantic consistency loss to facilitate the learning of high-level identity-related semantic features.
arXiv Detail & Related papers (2023-08-27T14:07:57Z) - Video Person Re-identification using Attribute-enhanced Features [49.68392018281875]
We propose a novel network architecture named Attribute Salience Assisted Network (ASA-Net) for attribute-assisted video person Re-ID.
To learn a better separation of the target from background, we propose to learn the visual attention from middle-level attribute instead of high-level identities.
arXiv Detail & Related papers (2021-08-16T07:41:27Z) - Cloth-Changing Person Re-identification from A Single Image with Gait
Prediction and Regularization [65.50321170655225]
We introduce Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations.
Experiments on image-based Cloth-Changing ReID benchmarks, e.g., LTCC, PRCC, Real28, and VC-Clothes, demonstrate that GI-ReID performs favorably against the state-of-the-arts.
arXiv Detail & Related papers (2021-03-29T12:10:50Z) - Apparel-invariant Feature Learning for Apparel-changed Person
Re-identification [70.16040194572406]
Most public ReID datasets are collected in a short time window in which persons' appearance rarely changes.
In real-world applications such as in a shopping mall, the same person's clothing may change, and different persons may wearing similar clothes.
It is critical to learn an apparel-invariant person representation under cases like cloth changing or several persons wearing similar clothes.
arXiv Detail & Related papers (2020-08-14T03:49:14Z) - Attribute-aware Identity-hard Triplet Loss for Video-based Person
Re-identification [51.110453988705395]
Video-based person re-identification (Re-ID) is an important computer vision task.
We introduce a new metric learning method called Attribute-aware Identity-hard Triplet Loss (AITL)
To achieve a complete model of video-based person Re-ID, a multi-task framework with Attribute-driven Spatio-Temporal Attention (ASTA) mechanism is also proposed.
arXiv Detail & Related papers (2020-06-13T09:15:38Z) - 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.