An Efficient Crowd Estimation Method Using Convolutional Neural Network with Thermal Images

Xu, Muhua

doi:10.1109/icsidp47821.2019.9172971

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The first was a speed-optimised Gaussian copula generator (hereafter “Fast ML”) and the other was a classical Gaussian copula generator (hereafter “Gaussian Copula”). Two non-parametric architectures were based on deep-learning (DL) neural networks; a Conditional Tabular Generative Adversarial Network (CTGAN) 12 and a Conditional Generative Adversarial Network incorporating Differential Privacy (DP-CGAN) 10 . The DP-CGAN was not part of SDV’s current library, but was built on top of existing SDV-library modules.…”

Section: Methodsmentioning

confidence: 99%

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Actionability of Synthetic Data in a Heterogeneous and Rare Healthcare Demographic; Adolescents and Young Adults (AYAs) with Cancer

Hogenboom,

Gomes,

Dekker

et al. 2024

Preprint

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Purpose: Research on rare diseases and atypical healthcare demographics is often slowed by high inter-subject heterogeneity and overall scarcity of data. Synthetic data (SD) has been proposed as means for data sharing, enlargement, and diversification, by artificially generating ′real′ phenomena while obscuring the ′real′ subject data. The utility of SD is actively scrutinised in healthcare research, but the role of sample size for actionability of SD is insufficiently explored. We aim to understand the interplay of actionability and sample size by generating SD sets of varying sizes from gradually diminishing amounts of real subjects′ data. We evaluate the actionability of SD in a highly heterogeneous and rare demographic: adolescents and young adults (AYAs) with cancer.Methodology: A population-based cross-sectional cohort study of 3735 AYAs was sub-sampled at random to produce 13 training datasets of varying sample sizes. We studied four distinct generator architectures built on the open-source Synthetic Data Vault library. Each architecture was used to generate SD of varying sizes based on each aforementioned training subsets. SD actionability was assessed by comparing the resulting SD to its respective ′real′ data against three metrics - veracity, utility, and privacy concealment.Results: All examined generator architectures yielded actionable data when generating SD with sizes similar to the ′real′ data. Large SD sample size increased veracity but generally increased privacy risks. Using fewer training subjects led to faster convergence in veracity, but partially exacerbated privacy concealment issues.Conclusion: SD is a potentially promising option for data sharing and data augmentation, yet sample size plays a significant role in its actionability. SD generation should go hand-in-hand with consistent scrutiny and sample size should be carefully considered in this process.

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Section: Methodsmentioning

confidence: 99%

“…While use of SD within healthcare is still in development, ethicists, policy makers, and researchers are trying to establish how SD can be used appropriately 5,8 . Meanwhile, various techniques 9–12 are now openly available which researchers can use to train generative models and thus create SD for various purposes 13–22 .…”

Section: Introductionmentioning

confidence: 99%

Actionability of Synthetic Data in a Heterogeneous and Rare Healthcare Demographic; Adolescents and Young Adults (AYAs) with Cancer

Hogenboom,

Gomes,

Dekker

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Since the images used are taken from drones, the pedestrian targets in the images are small, and there is no occlusion problem caused by vertical and oblique viewing angles. ere are also methods based on RGBD image CNN networks [43][44][45], because this method can reduce the occlusion problem caused by oblique viewing angles, and the error rate of crowd counting is low. However, the multicolumn structure leads to difficulty in training with many redundant parameters, and although the use of ensembles of CNNs can bring significant performance improvements, they come at the cost of a large amount of computation.…”

Section: Related Workmentioning

confidence: 99%

Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

Ren

2022

Scientific Programming

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Motivation. In the environment of day and night video surveillance, in order to improve the accuracy of machine vision dense crowd counting and target detection, this paper designs a day and night dual-purpose crowd counting and crowd detection network based on multimode image fusion. Methods. Two sub-models, RGBD-Net and RGBT-Net, are designed in this paper. The depth image features and thermal imaging features are effectively fused with the features of visible light images, so that the model has stronger anti-interference characteristics and robustness to the light noise interference caused by the sudden fall of light at night. The above models use density map regression-guided detection method to complete population counting and detection. Results. The model completed daytime training and testing on MICC dataset. Through verification, the average absolute error of the model was 1.025, the mean square error was 1.521, and the recall rate of target detection was 97.11%. Night vision training and testing were completed on the RGBT-CC dataset. After verification, the average absolute error of the network was 18.16, the mean square error was 32.14, and the recall rate of target detection was 97.65%. By verifying the effectiveness of the multimode medium-term fusion network, it is found to exceed the current most advanced bimodal fusion method. Conclusion. The experimental results show that the proposed multimodal fusion network can solve the counting and detection problem in the video surveillance environment during day and night. The ablation experiment further proves the effectiveness of the parameters of the two models.

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Real-Time People Counting Using IR-UWB Radar

Hasan

Ebrahim

Yuce

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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An Efficient Crowd Estimation Method Using Convolutional Neural Network with Thermal Images

Cited by 4 publications

References 8 publications

Actionability of Synthetic Data in a Heterogeneous and Rare Healthcare Demographic; Adolescents and Young Adults (AYAs) with Cancer

Actionability of Synthetic Data in a Heterogeneous and Rare Healthcare Demographic; Adolescents and Young Adults (AYAs) with Cancer

Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

Real-Time People Counting Using IR-UWB Radar

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