论文标题
快速：旋转意识的人在高架鱼眼图像中检测
RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images
论文作者
论文摘要
Fisheye图像在开销中检测到的最新方法要么使用径向对准的边界框来代表人们，假设人们总是沿图像半径出现，或者需要显着的预/后处理，从而从根本上提高了计算复杂性。在这项工作中，我们开发了一种名为Rapid的端到端旋转感知的人检测方法，该方法使用任意面向的边界框来检测人员。我们的全趋验神经网络使用周期性损耗函数直接回归每个边界框的角度，该损失函数说明了角度周期性。我们还创建了一个新的数据集，其中包含旋转边界框的时空注释，用于人们检测以及架空Fisheye视频中的其他视觉任务。我们表明，我们简单而有效的方法在三个Fisheye-image数据集上优于最先进的结果。代码和数据集可在http://vip.bu.edu/rapid上找到。
Recent methods for people detection in overhead, fisheye images either use radially-aligned bounding boxes to represent people, assuming people always appear along image radius or require significant pre-/post-processing which radically increases computational complexity. In this work, we develop an end-to-end rotation-aware people detection method, named RAPiD, that detects people using arbitrarily-oriented bounding boxes. Our fully-convolutional neural network directly regresses the angle of each bounding box using a periodic loss function, which accounts for angle periodicities. We have also created a new dataset with spatio-temporal annotations of rotated bounding boxes, for people detection as well as other vision tasks in overhead fisheye videos. We show that our simple, yet effective method outperforms state-of-the-art results on three fisheye-image datasets. Code and dataset are available at http://vip.bu.edu/rapid .
