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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/197246
Segmentation-guided privacy preservation in visual surveillance monitoring
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[en] Video surveillance has become a necessity to ensure safety and security. Today, with the advancement
of technology, video surveillance has become more accessible and widely available. Furthermore, it can be useful
in an enormous amount of applications and situations. For instance, it can be useful in ensuring public safety by
preventing vandalism, robbery, and shoplifting. The same applies to more intimate situations, like home monitoring to detect unusual behavior of residents or in similar situations like hospitals and assisted living facilities. Thus, cameras are installed in public places like malls, metro stations, and on-roads for traffic control, as well as in sensitive settings like hospitals, embassies, and private homes. Video surveillance has always been as-
sociated with the loss of privacy. Therefore, we developed a real-time visualization of privacy-protected video
surveillance data by applying a segmentation mask to protect privacy while still being able to identify existing
risk behaviors. This replaces existing privacy safeguards such as blanking, masking, pixelation, blurring, and
scrambling. As we want to protect human personal data that are visual such as appearance, physical information, clothing, skin, eye and hair color, and facial gestures. Our main aim of this work is to analyze and compare the most successful deep-learning-based state-of-the-art approaches for semantic segmentation. In this study, we
perform an efficiency-accuracy comparison to determine which segmentation methods yield accurate segmentation results while performing at the speed and execution required for real-life application scenarios. Furthermore, we also provide a modified dataset made from a combination of three existing datasets, COCO_stuff164K, PASCAL VOC 2012, and ADE20K, to make our comparison fair and generate privacyprotecting human segmentation masks.
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Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Sergio Escalera Guerrero, Zenjie Li i Kamal Nasrollahi
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GERYOUS FARES, Daniel. Segmentation-guided privacy preservation in visual surveillance monitoring. [consulted: 10 of October of 2026]. Available at: https://hdl.handle.net/2445/197246