NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences.

ABSTRACT: Visual place recognition (VPR) is critical in not only localization and mapping for autonomous driving vehicles, but also assistive navigation for the visually impaired population. To enable a long-term VPR system on a large scale, several challenges need to be addressed. First, different applications could require different image view directions, such as front views for self-driving cars while side views for the low vision people. Second, VPR in metropolitan scenes can often cause privacy concerns due to the imaging of pedestrian and vehicle identity information, calling for the need for data anonymization before VPR queries and database construction. Both factors could lead to VPR performance variations that are not well understood yet. To study their influences, we present the NYU-VPR d

SUBMITTER: Sheng D 

PROVIDER: S-EPMC9394449 | biostudies-literature | 2021 Sep-Oct

REPOSITORIES: biostudies-literature