论文标题
弗里达：鱼眼重新识别带有注释的数据集
FRIDA: Fisheye Re-Identification Dataset with Annotations
论文作者
论文摘要
从侧面安装的直线镜头摄像机的人重新识别（PRID）是一个充分的问题。另一方面，高架鱼眼摄像机的PRID是新的，并且基本上没有研究，这主要是由于缺乏合适的图像数据集。为了填补这一空白，我们介绍了“带注释的Fisheye重新识别数据集”（Frida），并用240k+边界盒子的注释，在大型室内空间中被3个时间同步，天花板安装的鱼眼摄像机捕获。由于视野重叠，在这种情况下，PRID与典型的PRID问题不同，我们深入讨论。我们还评估了Frida上10种最先进的PRID算法的性能。我们表明，对于6种CNN的算法，与对常见直流相机PRID数据集进行培训相比，对FRIDA的培训可提高地图的性能高达11.64％。
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.
