FRIDA: Fisheye Re-Identification Dataset with Annotations
This addresses the problem of PRID in fisheye camera systems for researchers, but it is incremental as it primarily provides a new dataset rather than a novel method.
The authors tackled the lack of datasets for person re-identification (PRID) from overhead fisheye cameras by introducing FRIDA, a dataset with 240k+ bounding-box annotations, and showed that training on it boosts performance by up to 11.64% points in mAP for CNN-based algorithms compared to using rectilinear-camera datasets.
Person re-identification (PRID) from side-mounted rectilinear-lens cameras is a well-studied problem. On the other hand, PRID from overhead fisheye cameras is new and largely unstudied, primarily due to the lack of suitable image datasets. To fill this void, we introduce the "Fisheye Re-IDentification Dataset with Annotations" (FRIDA), with 240k+ bounding-box annotations of people, captured by 3 time-synchronized, ceiling-mounted fisheye cameras in a large indoor space. Due to a field-of-view overlap, PRID in this case differs from a typical PRID problem, which we discuss in depth. We also evaluate the performance of 10 state-of-the-art PRID algorithms on FRIDA. We show that for 6 CNN-based algorithms, training on FRIDA boosts the performance by up to 11.64% points in mAP compared to training on a common rectilinear-camera PRID dataset.