在其他三个数据集上的实验结果 在ShanghaiTech Part A 数据集上定位实验结果 在NWPU-Crowd 数据集上的定位实验结果 密度图实验结果 距离标签可视化 由于是期刊,因此还有很多实验部分,我就不展示了,具体可以链接原文～ 论文信息 AutoScale: Learning to Scale for Crowd Counting...
Learn to scalePerson localizationDynamic cross-entropyRecent works on crowd counting mainly leverage Convolutional Neural Networks (CNNs) to count by regressing density maps, and have achieved great progress. In the density map, each person is represented by a Gaussian blob, and the final count is...
论文理解Leveraging Unlabeled Data for Crowd Counting by Learning to Rank,程序员大本营,技术文章内容聚合第一站。
The Xception Network pre-trained parameter is used as transfer learning to be trained again with the fully connected layers. CountNet then achieved a better crowd counting performance by training it with augmented dataset that robust to scale and slice variations. 展开 关键词:...
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank CVPR2018https://github.com/xialeiliu/CrowdCountingCVPR18 本文针对人群密度估计训练数据库规模很小的问题 提出了使用未标定数据来 self-supervised,具体通过 Learning to Rank 人群密度估计数据库规模很小的主要原因是 图像标记工作量很大,需要将图像...
Specifically, we use a two-stage framework, where we first learn a policy network to infer the perspective of the target scene, which outputs a scale label for the subsequent perspective normalization. Next, given the aligned inputs, we further adjust the scale-specific counting network to ...
Specifically,we use a two-stage framework, where we first learn a policy network to infer the perspective of the target scene, which outputs a scale label for the subsequent perspective normalization. Next, given the aligned inputs, we further adjust the scale-specific counting network to ...
Based on it, we build a large-scale, diverse synthetic dataset. Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using the real data, which ...
人群计数:SFCN--Learning from Synthetic Data for Crowd Counting in the Wild,程序员大本营,技术文章内容聚合第一站。
We need a visual surveillance that can automatically detect abnormality in crowd behavior so that the relevant action can be taken to prevent any public casualty. Basic steps required for crowd analysis is density estimation and crowd counting, object recognition, tracking, and anomaly detection in ...