Communication efficient model-aware federated learning for visual crowd counting and density estimation in smart cities
Federated learning (FL) is an attractive paradigm where a number of users can improve their
local models via sharing trained models or model increments with a central server, while the …
local models via sharing trained models or model increments with a central server, while the …
Federated learning for crowd counting in smart surveillance systems
Crowd counting in smart surveillance systems plays a crucial role in Internet of Things (IoT)
and smart cities, and can affect various aspects, such as public safety, crowd management …
and smart cities, and can affect various aspects, such as public safety, crowd management …
CrossCount: Efficient device-free crowd counting by leveraging transfer learning
Recently, wireless sensing is gaining immense attention in the Internet of Things (IoT) for
crowd counting and occupancy detection. As wireless signals propagate, they tend to scatter …
crowd counting and occupancy detection. As wireless signals propagate, they tend to scatter …
DA-Net: Learning the fine-grained density distribution with deformation aggregation network
The major challenges for accurate crowd counting stem from the large variations in the
scale, shape, and perspective. In fact, dealing with such difficulties depends on the …
scale, shape, and perspective. In fact, dealing with such difficulties depends on the …
On-board crowd counting and density estimation using low altitude unmanned aerial vehicles—looking beyond beating the benchmark
Recent advances in deep learning-based image processing have enabled significant
improvements in multiple computer vision fields, with crowd counting being no exception …
improvements in multiple computer vision fields, with crowd counting being no exception …
LCDnet: a lightweight crowd density estimation model for real-time video surveillance
Automatic crowd counting using density estimation has gained significant attention in
computer vision research. As a result, a large number of crowd counting and density …
computer vision research. As a result, a large number of crowd counting and density …
Crowd counting using deep learning in edge devices
Z Huang, R Sinnott, Q Ke - Proceedings of the 2021 IEEE/ACM 8th …, 2021 - dl.acm.org
Crowd counting is required for many situations and has historically been undertaken using
approximate (manual) estimations and measures. Deep learning allows to improve this …
approximate (manual) estimations and measures. Deep learning allows to improve this …
Vanishing region loss for crowd density estimation
B Yılmaz, SNHS Abdullah, VJ Kok - Pattern Recognition Letters, 2020 - Elsevier
Crowd density estimation is a crucial component in surveillance systems to construct safe
and efficient urban environments. Due to perspective distortion, individuals in crowd scenes …
and efficient urban environments. Due to perspective distortion, individuals in crowd scenes …
Federated learning based mobile crowd sensing with unreliable user data
Y Jiang, R Cong, C Shu, A Yang… - 2020 IEEE 22nd …, 2020 - ieeexplore.ieee.org
Mobile crowd sensing (MCS), as a novel paradigm that coordinates a crowd of distributed
devices to complete a whole sensing task, has attracted tremendous attention. While …
devices to complete a whole sensing task, has attracted tremendous attention. While …
Dual reconstructive autoencoder for crowd localization and estimation in density and fidt maps
This paper proposes crowd estimation technology to help authorities make the right
decisions in times of crisis. Specifically, deep learning models have faced these challenges …
decisions in times of crisis. Specifically, deep learning models have faced these challenges …