Results 11 to 20 of about 40,421 (274)
Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning
Applied Sciences, 2021 Counting people in crowd scenarios is extensively conducted in drone inspections, video surveillance, and public safety applications. Today, crowd count algorithms with supervised learning have improved significantly, but with a reliance on a large ...
Xiaoyu Hou +3 more
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Redesigned Skip-Network for Crowd Counting with Dilated Convolution and Backward Connection
Journal of Imaging, 2020 Crowd counting is a challenging task dealing with the variation of an object scale and a crowd density. Existing works have emphasized on skip connections by integrating shallower layers with deeper layers, where each layer extracts features in a ...
Sorn Sooksatra +3 more
doaj +1 more source
The Visual Social Distancing Problem [PDF]
, 2020 One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD). To comply with this constraint, workplaces, public institutions, transports and schools will likely adopt ...
Cristani, Marco +4 more
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Open Challenges for Crowd Density Estimation [PDF]
International Journal of Advanced Computer Science and Applications, 2020 Nowadays, many emergency systems and surveillance systems are related to the management of the crowd. The supervision of a crowded area presents a great challenge especially when the size of the crowd is unknown. This issue presents a point of start to the field of the estimation of the crowd based on density or counts. The density of a crowded area is
openaire +1 more source
Advances in Convolution Neural Networks Based Crowd Counting and Density Estimation
Big Data and Cognitive Computing, 2021 Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring.
Rafik Gouiaa +2 more
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Crowd counting via Multi-Scale Adversarial Convolutional Neural Networks
Journal of Intelligent Systems, 2020 The purpose of crowd counting is to estimate the number of pedestrians in crowd images. Crowd counting or density estimation is an extremely challenging task in computer vision, due to large scale variations and dense scene.
Zhu Liping +4 more
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Congestion-Aware Bayesian Loss for Crowd Counting
IEEE Access, 2022 Deep learning-based crowd density estimation can greatly improve the accuracy of crowd counting. Though a Bayesian loss method resolves the two problems of the need of a hand-crafted ground truth (GT) density and noisy annotations, counting accurately in
Jiyeoup Jeong +3 more
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Real-time crowd density mapping using a novel sensory fusion model of infrared and visual systems [PDF]
, 2013 Crowd dynamic management research has seen significant attention in recent years in research and industry in an attempt to improve safety level and management of large scale events and in large public places such as stadiums, theatres, railway stations ...
A. Al-Habaibeh +26 more
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Evaluating Crowd Density Estimators Via Their Uncertainty Bounds [PDF]
2019 IEEE International Conference on Image Processing (ICIP), 2019 In this work, we use the Belief Function Theory which extends the probabilistic framework in order to provide uncertainty bounds to different categories of crowd density estimators. Our method allows us to compare the multi-scale performance of the estimators, and also to characterize their reliability for crowd monitoring applications requiring ...
Vandoni, Jennifer +2 more
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IEEE Access, 2020 Nowadays, crowd analysis is one of the most important concepts that needs be relied upon, it contributes to decision making and ensuring the safety and security of the crowd.
Reem Alotaibi +5 more
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