meta-learningtest-time adaptationpseudo labelsdropoutMachine learning algorithms are commonly used for quickly and efficiently counting people from a crowd. Test-time adaptation methods for crowd counting adjust model parameters and employ additional data augmentation to better adapt the model to the ...
stanleykywu / covid-19-crowd-counting Star 1 Code Issues Pull requests Using transfer learning on pretrained image models to learn density map generation and count the number of people in an image. machine-learning python3 pytorch crowdcounting vgg16-model resnet50-32x32 Updated Nov 27, ...
Deep metric learning for crowdedness regression IEEE Trans. Circuits Syst. Video Technol. (2018) P. Viola et al. Detecting pedestrians using patterns of motion and appearance IEEE (2003) Detecting and counting people using real-time directional algorithms implemented by compute unified device architectu...
MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground Imagery.\url{https://arxiv.org/pdf/1909.12743.pdf}\\ [2] Yingying Zhang, Desen Zhou, Siqin Chen, Shenghua Gao, Yi Ma.2016.Single-Image Crowd Counting via Multi-Column Convolutional Neural Network.\url{https://zpascal....
Machine learning and deep learning Crowd density estimation - Crowd density estimation methods were classified into Direct methods based on model and trajectory and Indirect methods based on pixel counting and texture-based analysis.- Machine Learning and Deep Learning methods are based on the training...
Related... networks.提出使用交替学习密度图和人数估计来获得更好的局部最优解) Generating ranked image sets for counting 这一部分描述了如何系统地从未标记的人群 智能推荐 人群计数:COUNT Forest: CO-voting Uncertain Number of Targets using Random Forest for Crowd Density ...
“structured knowledge transfer” to train the model in an end-to-end manner. It aims to accelerate the fitting speed and enhance the learning ability of the student model. The crowd-counting system solution for edge computing is also proposed and implemented on an embedded device equipped with...
Marked Point Processes for Crowd Counting Delineate pedestrians in a foreground mask using shape coverings Adapt to different videos by learning the shape models rectangular covering Training samples Automatically learned shapes shape covering Weina Ge and Robert T. Collins Bayesian approach MPP prior Combi...
A benchmark for multi-class object counting and size estimation using deep convolutional neural networks 2022, Engineering Applications of Artificial Intelligence Citation Excerpt : Single-class object counting has been studied in a variety of applications where varied objects need to be counted within ...
Abrupt and continuous nature of scale variation in a crowded scene is a challenging task to enhance crowd counting accuracy in an image. Existing crowd counting techniques generally used multi-column or single-column dilated convolution to tackle scale variation due to perspective distortion. However,...