Long-term Visual Place Recognition

Farid Alijani, Jukka Peltomäki, Jussi Puura, Heikki Huttunen, Joni-Kristian Kämäräinen, Esa Rahtu

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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In this work, we study the long-term performance of visual place recognition in urban outdoor environment. A long-term benchmark is constructed from the Oxford RobotCar dataset. It contains sequences of the same route traversed over a period of approx. 500 days. We carefully selected three gallery sequences, one training sequence and 15 query sequences that cover different seasons, times of day and weather. The RobotCar sequences from the first half year have several problems, for example, only partial routes and inaccurate location data. We circumvent these problems by reversing the time. In the benchmark dataset the gallery and training images are the latest and the query sequences go gradually back in time. Our experiments provide the following findings. 1) the selected gallery sequence has strong impact on performance, and 2) additional training sequences help to mitigate differences between the gallery sequences. In addition, results indicate that 3) there is a long-term trend of performance degradation over time. The degradation can be quantified as about 6 percentage points per 100 days and, therefore, the initial performance of 40% eventually drops below 20% at the end.

Original languageEnglish
Title of host publication2022 26th International Conference on Pattern Recognition, ICPR 2022
Number of pages7
ISBN (Electronic)9781665490627
Publication statusPublished - 2022
Publication typeA4 Article in conference proceedings
EventInternational Conference on Pattern Recognition - Montreal, Canada
Duration: 21 Aug 202225 Aug 2022

Publication series

NameInternational Conference on Pattern Recognition
ISSN (Print)1051-4651


ConferenceInternational Conference on Pattern Recognition

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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