论文标题
基于视觉融合的时空融合的人重新识别架空鱼眼图像
Spatio-Visual Fusion-Based Person Re-Identification for Overhead Fisheye Images
论文作者
论文摘要
人重新识别（PRID）已在典型的监视场景中进行了彻底研究，在这些情况下，通过侧面安装的直线镜头摄像头对各种场景进行监视。迄今为止，对于安装在头顶上的鱼眼摄像机的方法很少，并且缺乏其性能。为了缩小这一性能差距，我们为Fisheye Prid提出了一个多功能的框架，在该框架中，我们通过新型功能融合结合了深度学习，基于颜色和基于位置的特征。我们评估了公共鱼眼PRID数据集Frida的各种功能组合的框架的性能。结果表明，我们的多种功能方法的表现优于最近基于外观的深度学习方法，而基于位置的方法的匹配准确性将近3％。我们还展示了拟议的PRID框架在大型，拥挤的室内空间中的潜在应用。
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.
