  Visual Place Recognition (VPR) is the ability to correctly recall a
previously visited place using visual information under environmental,
viewpoint and appearance changes. An emerging trend in VPR is the use of
sequence-based filtering methods on top of single-frame-based place matching
techniques for route-based navigation. The combination leads to varying levels
of potential place matching performance boosts at increased computational
costs. This raises a number of interesting research questions: How does
performance boost (due to sequential filtering) vary along the entire spectrum
of single-frame-based matching methods? How does sequence matching length
affect the performance curve? Which specific combinations provide a good
trade-off between performance and computation? However, there is lack of
previous work looking at these important questions and most of the
sequence-based filtering work to date has been used without a systematic
approach. To bridge this research gap, this paper conducts an in-depth
investigation of the relationship between the performance of single-frame-based
place matching techniques and the use of sequence-based filtering on top of
those methods. It analyzes individual trade-offs, properties and limitations
for different combinations of single-frame-based and sequential techniques. A
number of state-of-the-art VPR methods and widely used public datasets are
utilized to present the findings that contain a number of meaningful insights
for the VPR community.

Computer Vision and Pattern Recognition