  Person re-identification (PRID) has been thoroughly researched in typical
surveillance scenarios where various scenes are monitored by side-mounted,
rectilinear-lens cameras. To date, few methods have been proposed for fisheye
cameras mounted overhead and their performance is lacking. In order to close
this performance gap, we propose a multi-feature framework for fisheye PRID
where we combine deep-learning, color-based and location-based features by
means of novel feature fusion. We evaluate the performance of our framework for
various feature combinations on FRIDA, a public fisheye PRID dataset. The
results demonstrate that our multi-feature approach outperforms recent
appearance-based deep-learning methods by almost 18% points and location-based
methods by almost 3% points in matching accuracy. We also demonstrate the
potential application of the proposed PRID framework to people counting in
large, crowded indoor spaces.
