Related papers: Aerial-Ground Person Re-ID

Aerial-Ground Person Re-ID

URL: http://arxiv.org/abs/2303.08597v5
Date: Mon, 14 Aug 2023 04:44:50 GMT
Title: Aerial-Ground Person Re-ID
Authors: Huy Nguyen, Kien Nguyen, Sridha Sridharan, Clinton Fookes
Abstract summary: We propose a new benchmark dataset - AG-ReID, which performs person re-ID matching in a new setting: across aerial and ground cameras. Our dataset contains 21,983 images of 388 identities and 15 soft attributes for each identity. The data was collected by a UAV flying at altitudes between 15 to 45 meters and a ground-based CCTV camera on a university campus.
Score: 43.241435887373804
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Person re-ID matches persons across multiple non-overlapping cameras. Despite the increasing deployment of airborne platforms in surveillance, current existing person re-ID benchmarks' focus is on ground-ground matching and very limited efforts on aerial-aerial matching. We propose a new benchmark dataset - AG-ReID, which performs person re-ID matching in a new setting: across aerial and ground cameras. Our dataset contains 21,983 images of 388 identities and 15 soft attributes for each identity. The data was collected by a UAV flying at altitudes between 15 to 45 meters and a ground-based CCTV camera on a university campus. Our dataset presents a novel elevated-viewpoint challenge for person re-ID due to the significant difference in person appearance across these cameras. We propose an explainable algorithm to guide the person re-ID model's training with soft attributes to address this challenge. Experiments demonstrate the efficacy of our method on the aerial-ground person re-ID task. The dataset will be published and the baseline codes will be open-sourced at https://github.com/huynguyen792/AG-ReID to facilitate research in this area.

Related papers

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)
AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identification [39.58286453178339]
Aerial-ground person re-identification (Re-ID) presents unique challenges in computer vision. We introduce AG-ReID.v2, a dataset specifically designed for person Re-ID in mixed aerial and ground scenarios. This dataset comprises 100,502 images of 1,615 unique individuals, each annotated with matching IDs and 15 soft attribute labels.
arXiv Detail & Related papers (2024-01-05T04:53:33Z)
Ground-to-Aerial Person Search: Benchmark Dataset and Approach [42.54151390290665]
We construct a large-scale dataset for Ground-to-Aerial Person Search, named G2APS. G2APS contains 31,770 images of 260,559 annotated bounding boxes for 2,644 identities appearing in both of the UAVs and ground surveillance cameras.
arXiv Detail & Related papers (2023-08-24T11:11:26Z)
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)
Domain Adaptive Egocentric Person Re-identification [10.199631830749839]
Person re-identification (re-ID) in first-person (egocentric) vision is a fairly new and unexplored problem. With the increase of wearable video recording devices, egocentric data becomes readily available. There is a significant lack of large scale structured egocentric datasets for person re-identification.
arXiv Detail & Related papers (2021-03-08T16:19:32Z)
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)
Unsupervised Pre-training for Person Re-identification [90.98552221699508]
We present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" We make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation.
arXiv Detail & Related papers (2020-12-07T14:48:26Z)
PoseTrackReID: Dataset Description [97.7241689753353]
Pose information is helpful to disentangle useful feature information from background or occlusion noise. With PoseTrackReID, we want to bridge the gap between person re-ID and multi-person pose tracking. This dataset provides a good benchmark for current state-of-the-art methods on multi-frame person re-ID.
arXiv Detail & Related papers (2020-11-12T07:44:25Z)
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.