NPR: Nocturnal Place Recognition Using Nighttime Translation in Large-Scale Training Procedures

IF 8.7 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-03-20 DOI:10.1109/JSTSP.2024.3403247
Bingxi Liu;Yujie Fu;Feng Lu;Jinqiang Cui;Yihong Wu;Hong Zhang
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Abstract

Visual Place Recognition (VPR) is a critical task within the fields of intelligent robotics and computer vision. It involves retrieving similar database images based on a query photo from an extensive collection of known images. In real-world applications, this task encounters challenges when dealing with extreme illumination changes caused by nighttime query images. However, a large-scale training set with day-night correspondence for VPR remains absent. To address this challenge, we propose a novel pipeline that divides the general VPR into distinct domains of day and night, subsequently conquering Nocturnal Place Recognition (NPR). Specifically, we first establish a daynight street scene dataset, named NightStreet, and use it to train an unpaired image-to-image translation model. Then, we utilize this model to process existing large-scale VPR datasets, generating the night version of VPR datasets and demonstrating how to combine them with two popular VPR pipelines. Finally, we introduce a divide-and-conquer VPR framework designed to solve the degradation of NPR during daytime conditions. We provide comprehensive explanations at theoretical, experimental, and application levels. Under our framework, the performance of previous methods can be significantly improved on two public datasets, including the top-ranked method.
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NPR:在大规模训练程序中使用夜间翻译进行夜间地点识别
视觉位置识别(VPR)是智能机器人和计算机视觉领域的一项重要任务。它包括根据查询照片从大量已知图像中检索类似的数据库图像。在实际应用中,这项任务在处理夜间查询图像引起的极端光照变化时会遇到挑战。然而,用于 VPR 的大规模昼夜对应训练集仍然缺失。为了应对这一挑战,我们提出了一种新颖的方法,将一般的 VPR 分成白天和黑夜两个不同的领域,从而实现夜间地点识别(NPR)。具体来说,我们首先建立了一个名为 "NightStreet "的日夜街道场景数据集,并用它来训练一个无配对图像到图像的翻译模型。然后,我们利用该模型处理现有的大规模 VPR 数据集,生成夜间版 VPR 数据集,并演示如何将它们与两种流行的 VPR 管道相结合。最后,我们介绍了一个分而治之的 VPR 框架,旨在解决白天条件下的 NPR 退化问题。我们从理论、实验和应用层面进行了全面解释。在我们的框架下,以往方法在两个公共数据集上的性能得到了显著提高,其中包括排名第一的方法。
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来源期刊
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing 工程技术-工程:电子与电气
CiteScore
19.00
自引率
1.30%
发文量
135
审稿时长
3 months
期刊介绍: The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others. The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.
期刊最新文献
Front Cover Table of Contents IEEE Signal Processing Society Information Introduction to the Special Issue Near-Field Signal Processing: Algorithms, Implementations and Applications IEEE Signal Processing Society Information
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