1 research outputs found
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning
Most of the existing approaches for person re-identification consider a
static setting where the number of cameras in the network is fixed. An
interesting direction, which has received little attention, is to explore the
dynamic nature of a camera network, where one tries to adapt the existing
re-identification models after on-boarding new cameras, with little additional
effort. There have been a few recent methods proposed in person
re-identification that attempt to address this problem by assuming the labeled
data in the existing network is still available while adding new cameras. This
is a strong assumption since there may exist some privacy issues for which one
may not have access to those data. Rather, based on the fact that it is easy to
store the learned re-identifications models, which mitigates any data privacy
concern, we develop an efficient model adaptation approach using hypothesis
transfer learning that aims to transfer the knowledge using only source models
and limited labeled data, but without using any source camera data from the
existing network. Our approach minimizes the effect of negative transfer by
finding an optimal weighted combination of multiple source models for
transferring the knowledge. Extensive experiments on four challenging benchmark
datasets with a variable number of cameras well demonstrate the efficacy of our
proposed approach over state-of-the-art methods.Comment: Accepted to CVPR 202