A review on visual privacy preservation techniques for active and assisted living

Ravi, Siddharth; Climent-Pérez, Pau; Flórez-Revuelta, Francisco

doi:10.1007/s11042-023-15775-2

Cited by 19 publications

(4 citation statements)

References 118 publications

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“…On the other hand, some fire detection models have been implemented in actual industries, although the welding spark detection system has not been fully tested and demonstrated in actual construction because it is still in the exploratory phase of study. Prior to implementing the proposed welding spark detection model at a construction site, it may be necessary to integrate video privacy models, such as facial or body deidentification in images [ 58 , 59 ] to ensure compliance with privacy regulations. Additionally, it is important to consider potential issues such as detection errors and biased data.…”

Section: Discussionmentioning

confidence: 99%

Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters

Jin,

Ahn,

Kim

et al. 2023

Sensors

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One of the primary causes of fires at construction sites is welding sparks. Fire detection systems utilizing computer vision technology offer a unique opportunity to monitor fires in construction sites. However, little effort has been made to date in regard to real-time tracking of small sparks that can lead to major fires at construction sites. In this study, a novel method is proposed to detect welding sparks in real-time contour detection with deep learning parameter tuning. An automatic parameter tuning algorithm employing a convolutional neural network was developed to identify the optimum hue saturation value. Additional filtering methods regarding the non-welding zone and a contour area-based filter were also newly developed to enhance the accuracy of welding spark prediction. The method was evaluated using 230 welding spark images and 104 videos. The results obtained from the welding images indicate that the suggested model for detecting welding sparks achieves a precision of 74.45% and a recall of 63.50% when noise images, such as flashing and reflection light, were removed from the dataset. Furthermore, our findings demonstrate that the proposed model is effective in capturing the number of welding sparks in the video dataset, with a 95.2% accuracy in detecting the moment when the number of welding sparks reaches its peak. These results highlight the potential of automated welding spark detection to enhance fire surveillance at construction sites.

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Section: Discussionmentioning

confidence: 99%

Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters

Jin,

Ahn,

Kim

et al. 2023

Sensors

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“…When exposed to cameras during capture, these artifacts increase the likelihood of the subject being erroneously identified, effectively enhancing the odds of misidentification. This and many more approaches related to visual privacy preservation are thoroughly reviewed by Ravi & Climent-Pérez (2023).…”

Section: Methods Of Implementing Video Obfuscationmentioning

confidence: 99%

Architecture of an IP camera system using machine learning for privacy protection

Nikolić,

Popović,

Ubavić

et al. 2023

Conference Proceedings : ICEMIT

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The development of technologies and the Internet conditioned the development of video surveillance systems. In modern video surveillance systems, the complete content recorded by a camera can be publicly available, i.e., visible on the Internet. The mass use of this type of camera has led to the fact that the cameras record everything and everyone and that the recorded content violates privacy. In order to protect privacy, different types of technologies and software are applied. One of those technologies is machine learning. By applying machine learning, it is possible to mask people's faces on recorded material. In this way, in publicly available content, as well as in case of unauthorized access, the identity of people is not compromised. This paper describes the architecture of a system that enables the application of machine learning for the purpose of protecting the identity of a person, where only an authorized person would have access to the original unmasked content.

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“…It is worth noting that when ambient intelligence comes into play, privacy becomes an issue not only for patients and parents but also for clinicians and caregivers. Addressing this issue at the technical level requires the adoption of privacypreservation approaches such as those based on privacy-preserving visual sensors (e.g., depth or thermal sensors) or those based on ad hoc techniques able to ensure context-aware visual privacy and retain all the information contained in RGB cameras [166]. This may help reduce the feeling of intrusion in parents and caregivers.…”

Section: Ethico-legal Considerationsmentioning

confidence: 99%

Computer Vision Tasks for Ambient Intelligence in Children’s Health

Germanese,

Colantonio,

Del Coco

et al. 2023

Information

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Computer vision is a powerful tool for healthcare applications since it can provide objective diagnosis and assessment of pathologies, not depending on clinicians’ skills and experiences. It can also help speed-up population screening, reducing health care costs and improving the quality of service. Several works summarise applications and systems in medical imaging, whereas less work is devoted to surveying approaches for healthcare goals using ambient intelligence, i.e., observing individuals in natural settings. Even more, there is a lack of papers providing a survey of works exhaustively covering computer vision applications for children’s health, which is a particularly challenging research area considering that most existing computer vision technologies have been trained and tested only on adults. The aim of this paper is then to survey, for the first time in the literature, the papers covering children’s health-related issues by ambient intelligence methods and systems relying on computer vision.

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A review on visual privacy preservation techniques for active and assisted living

Cited by 19 publications

References 118 publications

Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters

Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters

Architecture of an IP camera system using machine learning for privacy protection

Computer Vision Tasks for Ambient Intelligence in Children’s Health

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