Results 41 to 50 of about 322,190 (190)
IEEE Access, 2019 This paper proposes an automatic scale-adaptive approach with attention mechanism-based crowd spatial information addressing the crowd counting task, i.e. a novel cascaded crowd counting network.
Weihang Kong +3 more
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Wi-CaL: WiFi Sensing and Machine Learning Based Device-Free Crowd Counting and Localization
IEEE Access, 2022 Wireless sensing represented by WiFi channel state information (CSI) is now enabling various fields of applications such as person identification, human activity recognition, occupancy detection, localization, and crowd estimation these days.
Hyuckjin Choi +4 more
doaj +1 more source
Domain-General Crowd Counting in Unseen Scenarios [PDF]
, 2023 Domain shift across crowd data severely hinders crowd counting models to generalize to unseen scenarios. Although domain adaptive crowd counting approaches close this gap to a certain extent, they are still dependent on the target domain data to adapt (e.
Deng, Jiankang +2 more
core +2 more sources
On Crowd Density Estimation for Surveillance
, 2006 The goal of this work is to use computer vision to measure crowd density in outdoor scenes. Crowd density estimation is an important task in crowd monitoring. The assessment is carried out using images of a graduation scene which illustrated variation of
Rahmalan, Hidayah +5 more
core +2 more sources
TransCrowd: weakly-supervised crowd counting with transformers [PDF]
, 2022 The mainstream crowd counting methods usually utilize the convolution neural network (CNN) to regress a density map, requiring point-level annotations. However, annotating each person with a point is an expensive and laborious process. During the testing
Bai, Xiang +4 more
core +1 more source
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
An evaluation of crowd counting methods, features and regression models [PDF]
, 2015 Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and
Fookes, Clinton B. +3 more
core +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
Fine-Grained Counting with Crowd-Sourced Supervision [PDF]
, 2022 Crowd-sourcing is an increasingly popular tool for image analysis in animal ecology. Computer vision methods that can utilize crowd-sourced annotations can help scale up analysis further.
Hart, Tom +2 more
core +1 more source
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