通过不确定性特征融合方法和自动加权测量组合增强人员再识别能力

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Quang-Huy Che, Le-Chuong Nguyen, Duc-Tuan Luu, Vinh-Tiep Nguyen
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引用次数: 0

摘要

人员再识别(Re-ID)是一项具有挑战性的任务,涉及在监控系统的不同摄像机视图中识别同一个人。目前的方法通常依赖于单摄像头视图的特征,在处理多摄像头以及视点变化和遮挡等挑战时,这种方法可能会受到限制。本文介绍了一种新方法,通过不确定特征融合方法(UFFM)和自动加权测量组合(AMC)增强 ReID 模型的能力。UFFM 使用从多幅图像中独立提取的特征生成多视图特征,以减轻视图偏差。然而,仅仅依靠基于多视角特征的相似性是有限的,因为这些特征忽略了单视角特征所代表的细节。因此,我们提出了 AMC 方法,通过结合各种测量方法来生成更稳健的相似性测量方法。在人物再识别数据集上进行评估时,我们的方法大大提高了 Rank@1(Rank-1 精确度)和平均精确度(mAP)。在具有挑战性的数据集上与 BoT 基准相结合,我们取得了令人印象深刻的结果,在 MSMT17 数据集上,Rank@1 提高了 7.9%,mAP 提高了 12.1%。在 Occluded-DukeMTMC 数据集上,我们的方法将 Rank@1 提高了 22.0%,mAP 提高了 18.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing person re-identification via Uncertainty Feature Fusion Method and Auto-weighted Measure Combination
Person re-identification (Re-ID) is a challenging task that involves identifying the same person across different camera views in surveillance systems. Current methods usually rely on features from single-camera views, which can be limiting when dealing with multiple cameras and challenges such as changing viewpoints and occlusions. In this paper, a new approach is introduced that enhances the capability of ReID models through the Uncertain Feature Fusion Method (UFFM) and Auto-weighted Measure Combination (AMC). UFFM generates multi-view features using features extracted independently from multiple images to mitigate view bias. However, relying only on similarity based on multi-view features is limited because these features ignore the details represented in single-view features. Therefore, we propose the AMC method to generate a more robust similarity measure by combining various measures. Our method significantly improves Rank@1 (Rank-1 accuracy) and Mean Average Precision (mAP) when evaluated on person re-identification datasets. Combined with the BoT Baseline on challenging datasets, we achieve impressive results, with a 7.9% improvement in Rank@1 and a 12.1% improvement in mAP on the MSMT17 dataset. On the Occluded-DukeMTMC dataset, our method increases Rank@1 by 22.0% and mAP by 18.4%.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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