Crowd counting using group tracking and local features [PDF]
, 2010 In public venues, crowd size is a key indicator of crowd safety and stability. In this paper we propose a crowd counting algorithm that uses tracking and local features to count the number of people in each group as represented by a foreground blob ...
Denman, Simon +3 more
core +1 more source
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions [PDF]
, 2020 Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail to capture informative features in extreme conditions, e.g., imaging at ...
Dou, Dejing +6 more
core +1 more source
Performance of video processing at the edge for crowd-monitoring applications [PDF]
, 2018 Video analytics has a key role to play in smart cities and connected community applications such as crowd counting, activity detection, event classification, traffic counting etc.
Ballas, Camille +4 more
core +1 more source
Crowd-Centric Counting via Unsupervised Learning [PDF]
2019 IEEE International Conference on Communications Workshops (ICC Workshops), 2019 RYC-2016 ...
MORSELLI, Flavio +4 more
openaire +5 more sources
Improved Crowd Counting Method Based on Scale-Adaptive Convolutional Neural Network
IEEE Access, 2019 Crowd counting is a challenging task due to the influence of various factors, such as scene transformation, complex crowd distribution, uneven illumination, and occlusion.
Jun Sang +6 more
doaj +1 more source
Crowd counting using a self‐attention multi‐scale cascaded network
IET Computer Vision, 2019 Recent developments of crowd analysis and behaviour prediction have attracted much attention. Crowd counting, as the essential and challenging task in crowd analysis, is riddled with many issues, such as large scale variations, serious occlusion, and so ...
He Li, Shihui Zhang, Weihang Kong
doaj +1 more source
AVMSN: An Audio-Visual Two Stream Crowd Counting Framework Under Low-Quality Conditions
IEEE Access, 2021 Crowd counting is considered as the essential computer vision application that uses the convolutional neural network to model the crowd density as the regression task.
Ruihan Hu +8 more
doaj +1 more source
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
, 2018 In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability in congested areas is downgraded ...
Gao, Chenqiang +3 more
core +1 more source
Crowdsourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears [PDF]
, 2012 Background: There are 600,000 new malaria cases daily worldwide. The gold standard for estimating the parasite burden and the corresponding severity of the disease consists in manually counting the number of parasites in blood smears through a microscope,
Abt, C +10 more
core +2 more sources
Mask Guided GAN for Density Estimation and Crowd Counting
IEEE Access, 2020 Density estimation aims to predict the spatial distribution of a crowd scene, and crowd counting aims to automatically check the number of heads as close as the ground truth.
Hai-Yan Yao, Wang-Gen Wan, Xiang Li
doaj +1 more source