论文标题
DPDNET：使用高间接深度相机的深度学习使用强大的人探测器
DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera
论文作者
论文摘要
在本文中，我们提出了一种基于深度学习的方法，该方法从具有高可靠性的单个间接费用深度图像中检测到多人。我们的神经网络称为DPDNET，基于两个基于残余层的两个完全跨跨编码器折线神经块。主块将深度图像作为输入，并生成像素置信度图，其中图像中的每个检测到的人都由高斯样分布表示。改进块结合了深度图像和主块的输出，以完善置信图。使用深度图像和头部位置标签同时对两个块进行了端到端训练。实验工作表明，DPDNET的表现优于最先进的方法，在三个不同的公开数据集中，精确度大于99％，而没有重新调整。此外，我们提案的计算复杂性与现场人数无关，并使用常规GPU实时运行。
In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, is based on two fully-convolutional encoder-decoder neural blocks based on residual layers. The Main Block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution. The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and head position labels. The experimental work shows that DPDNet outperforms state-of-the-art methods, with accuracies greater than 99% in three different publicly available datasets, without retraining not fine-tuning. In addition, the computational complexity of our proposal is independent of the number of people in the scene and runs in real time using conventional GPUs.
