Automated training of location-specific edge models for traffic counting
Deep neural networks are the state of the art for various machine learning problems dealing
with large amounts of rich sensor data. It is often desirable to evaluate these models on …
with large amounts of rich sensor data. It is often desirable to evaluate these models on …
Efficient and switchable CNN for crowd counting based on embedded terminal
Crowd counting plays an important role in urban management and public security. Recently,
deep learning has shown a great advantage in making the quality of crowd counting more …
deep learning has shown a great advantage in making the quality of crowd counting more …
A spatio-temporal attentive network for video-based crowd counting
Automatic people counting from images has recently drawn attention for urban monitoring in
modern Smart Cities due to the ubiquity of surveillance camera networks. Current computer …
modern Smart Cities due to the ubiquity of surveillance camera networks. Current computer …
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 …
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 …
The density-aware estimation network for vehicle counting in traffic surveillance system
S Sooksatra, A Yoshitaka, T Kondo… - … Conference on Signal …, 2019 - ieeexplore.ieee.org
In a surveillance system, the performance of vehicle counting is typically sensitive to the
different patterns of traffic density. To address this issue, we proposed the estimation …
different patterns of traffic density. To address this issue, we proposed the estimation …
Towards perspective-free object counting with deep learning
D Onoro-Rubio, RJ López-Sastre - … The Netherlands, October 11–14, 2016 …, 2016 - Springer
In this paper we address the problem of counting objects instances in images. Our models
are able to precisely estimate the number of vehicles in a traffic congestion, or to count the …
are able to precisely estimate the number of vehicles in a traffic congestion, or to count the …
Pedestrian counting using deep models trained on synthetically generated images
Counting pedestrians in surveillance applications is a common scenario. However, it is often
challenging to obtain sufficient annotated training data, especially so for creating models …
challenging to obtain sufficient annotated training data, especially so for creating models …
Almost unsupervised learning for dense crowd counting
DB Sam, NN Sajjan, H Maurya, RV Babu - Proceedings of the AAAI …, 2019 - aaai.org
We present an unsupervised learning method for dense crowd count estimation. Marred by
large variability in appearance of people and extreme overlap in crowds, enumerating …
large variability in appearance of people and extreme overlap in crowds, enumerating …
Dense vehicle counting method based on deep spatio-temporal network
Accurate estimation of the number of dense objects such as crowds or vehicles in an image
is a meaningful research task, which has been applied in many fields such as safety …
is a meaningful research task, which has been applied in many fields such as safety …