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Visual place recognition (VPR) is critical in not only localization andmapping for autonomous driving vehicles, but also in assistive navigation forthe visually impaired population. To enable a long-term VPR system on a largescale, several challenges need to be addressed. First, different applicationscould require different image view directions, such as front views forself-driving cars while side views for the low vision people. Second, VPR inmetropolitan scenes can often cause privacy concerns due to the imaging ofpedestrian and vehicle identity information, calling for the need for dataanonymization before VPR queries and database construction. Both factors couldlead to VPR performance variations that are not well understood yet. To studytheir influences, we present the NYU-VPR dataset that contains more than200,000 images over a 2km by 2km area near the New York University campus,taken within the whole year of 2016. We present benchmark results on severalpopular VPR algorithms showing that side views are significantly morechallenging for current VPR methods while the influence of data anonymizationis almost negligible, together with our hypothetical explanations and in-depthanalysis.

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