An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-Identification

• URL: http://arxiv.org/abs/2403.15119v1
• Date: Fri, 22 Mar 2024 11:21:51 GMT
• Title: An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-Identification
• Authors: Lei Zhang, Xiaowei Fu, Fuxiang Huang, Yi Yang, Xinbo Gao,
• Abstract summary: Person re-identification (ReID) has made great strides thanks to the data-driven deep learning techniques. The existing benchmark datasets lack diversity, and models trained on these data cannot generalize well to dynamic wild scenarios. We develop a new Open-World, Diverse, Cross-Spatial-Temporal dataset named OWD with several distinct features.
• Score: 58.5877965612088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person re-identification (ReID) has made great strides thanks to the data-driven deep learning techniques. However, the existing benchmark datasets lack diversity, and models trained on these data cannot generalize well to dynamic wild scenarios. To meet the goal of improving the explicit generalization of ReID models, we develop a new Open-World, Diverse, Cross-Spatial-Temporal dataset named OWD with several distinct features. 1) Diverse collection scenes: multiple independent open-world and highly dynamic collecting scenes, including streets, intersections, shopping malls, etc. 2) Diverse lighting variations: long time spans from daytime to nighttime with abundant illumination changes. 3) Diverse person status: multiple camera networks in all seasons with normal/adverse weather conditions and diverse pedestrian appearances (e.g., clothes, personal belongings, poses, etc.). 4) Protected privacy: invisible faces for privacy critical applications. To improve the implicit generalization of ReID, we further propose a Latent Domain Expansion (LDE) method to develop the potential of source data, which decouples discriminative identity-relevant and trustworthy domain-relevant features and implicitly enforces domain-randomized identity feature space expansion with richer domain diversity to facilitate domain invariant representations. Our comprehensive evaluations with most benchmark datasets in the community are crucial for progress, although this work is far from the grand goal toward open-world and dynamic wild applications.

Related papers

• Towards Global Localization using Multi-Modal Object-Instance Re-Identification [23.764646800085977]
  We propose a novel re-identification transformer architecture that integrates multimodal RGB and depth information. We demonstrate improvements in ReID across scenes that are cluttered or have varying illumination conditions. We also develop a ReID-based localization framework that enables accurate camera localization and pose identification across different viewpoints.
  arXiv  Detail & Related papers  (2024-09-18T14:15:10Z)
• Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-Training [51.87027943520492]
  We present a novel paradigm Diffusion-ReID to efficiently augment and generate diverse images based on known identities. Benefiting from our proposed paradigm, we first create a new large-scale person Re-ID dataset Diff-Person, which consists of over 777K images from 5,183 identities.
  arXiv  Detail & Related papers  (2024-06-10T06:26:03Z)
• ENTIRe-ID: An Extensive and Diverse Dataset for Person Re-Identification [0.46040036610482665]
  ENTIRe-ID dataset comprises over 4.45 million images from 37 different cameras in varied environments. This dataset is uniquely designed to tackle the challenges of domain variability and model generalization. This design ensures a realistic and robust training platform for ReID models.
  arXiv  Detail & Related papers  (2024-05-30T20:26:47Z)
• All in One Framework for Multimodal Re-identification in the Wild [58.380708329455466]
  multimodal learning paradigm for ReID introduced, referred to as All-in-One (AIO) AIO harnesses a frozen pre-trained big model as an encoder, enabling effective multimodal retrieval without additional fine-tuning. Experiments on cross-modal and multimodal ReID reveal that AIO not only adeptly handles various modal data but also excels in challenging contexts.
  arXiv  Detail & Related papers  (2024-05-08T01:04:36Z)
• GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image [94.56927147492738]
  We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes from single images. We show that leveraging diffusion priors can markedly improve generalization, detail preservation, and efficiency in resource usage. We propose a simple yet effective strategy to segregate the complex data distribution of various scenes into distinct sub-distributions.
  arXiv  Detail & Related papers  (2024-03-18T17:50:41Z)
• Benchmarking person re-identification datasets and approaches for practical real-world implementations [1.0079626733116613]
  Person Re-Identification (Re-ID) has received a lot of attention. However, when such Re-ID models are deployed in new cities or environments, the task of searching for people within a network of security cameras is likely to face an important domain shift. This paper introduces a complete methodology to evaluate Re-ID approaches and training datasets with respect to their suitability for unsupervised deployment for live operations.
  arXiv  Detail & Related papers  (2022-12-20T03:45:38Z)
• Semi-Supervised Domain Generalizable Person Re-Identification [74.75528879336576]
  Existing person re-identification (re-id) methods are stuck when deployed to a new unseen scenario. Recent efforts have been devoted to domain adaptive person re-id where extensive unlabeled data in the new scenario are utilized in a transductive learning manner. We aim to explore multiple labeled datasets to learn generalized domain-invariant representations for person re-id.
  arXiv  Detail & Related papers  (2021-08-11T06:08:25Z)
• Person Re-identification based on Robust Features in Open-world [0.0]
  We propose a low-cost and high-efficiency method to solve shortcomings of the existing re-ID research. Our approach based on pose estimation model improved by group convolution to obtain the continuous key points of pedestrian. Our method achieves Rank-1: 60.9%, Rank-5: 78.1%, and mAP: 49.2% on this dataset, which exceeds most existing state-of-art re-ID models.
  arXiv  Detail & Related papers  (2021-02-22T06:49:28Z)
• Deep Learning for Person Re-identification: A Survey and Outlook [233.36948173686602]
  Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings.
  arXiv  Detail & Related papers  (2020-01-13T12:49:22Z)

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.