| Summary: | Person Re-Identification (Person Re-ID) is a challenge which main goal relates to the matching
of person images obtained from various cameras. Person Re-ID is growing in importance in
several key fields relating to homeland security, surveillance, and sports performance. Cross
Perspective Person Re-ID delves specifically into when the matching of person images when
the images are gathered at different viewpoints from one another. Currently datasets linked to
Person Re-ID do not consider issues within the cross-perspective realm. As it is primarily
focused on recognising people from a similar vantage point.
This report builds upon a previously collected dataset from Nanyang Technological University,
expanding upon this with the collection of a new dataset. This new cross-perspective dataset is
approximately twice as large as the preliminary dataset. This can be used as a more
comprehensive testing dataset where models trained upon the existing public datasets can be
experimented in cross-perspective specific challenges. The three targeted problems that are
discussed in this report concerns differences in Visual features, where from a higher viewpoint
some characteristics intrinsic to a person can be obscured. A second challenge is in person
alignment- at high drone perspective person images are tilted at a steep angle which lowers
efficiency of models. Lastly as drone images are taken physically at a further distance to ground
images the images of people are lower in resolution as they consume less on-screen pixels.
Utilising a DEX framework resulted in an improvement of 14% Rank-1 and 24% mAP for
mitigating view changes challenges. Recorded improvements of 3-6% in Rank-1 score by
aligning non-orthogonal angles to a vertical position, addressing the challenge of person
alignment at high drone perspectives. Switching from a ResNet-50 backbone to a HRNet
backbone improvements of 6-12 % and 1-4% for Rank-1 and mAP metrics respectively have
been attained. By looking at these issues an optimal model has been suggested along with future
work to further improve cross-perspective person re-ID results.
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