Enhancing Lifelogging Privacy by Detecting Screens

By Mohammed Korayem, Robert Templeman, Dennis Chen, David J. Crandall, Apu Kapadia. 2016

86 citations recorded by the source corpus.

Low-cost, lightweight wearable cameras let us record (or 'lifelog') our lives from a 'first-person' perspective for purposes ranging from fun to therapy. But they also capture private information that people may not want to be recorded, especially if images are stored in the cloud or visible to other people. For example, recent studies suggest that computer screens may be lifeloggers' single greatest privacy concern, because many people spend a considerable amount of time in front of devices that display private information. In this paper, we investigate using computer vision to automatically detect computer screens in photo lifelogs. We evaluate our approach on an existing in-situ dataset of 36 people who wore cameras for a week, and show that our technique could help manage privacy in the upcoming era of wearable cameras.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Defeating Image Obfuscation with Deep Learning (2016, methodology): “Korayem et al. [22] proposed a technique to detect computer screens in images.”
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption (2017, background): “Recent work includes sensitive place detection [19, 35, 55] and privacy-preserving activity recognition [49].”
Deepdiary: Lifelogging image captioning and summarization (2018, methodology): “Work in computer vision has applied traditional supervised recognition techniques to this problem in order to detect pre-defined sets of objects [30,52], scene types [31], and places [86].”
EgoVQA - An Egocentric Video Question Answering Benchmark Dataset (2019, background): “Recent computer vision research topics on studying first-person videos have included object detection [19,24,25,39], hands detection [10], scene understanding [26], activity recognition [23, 47], person identification and segmentation [20, 40, 57, 58], gaze detection and tracking [34, 62], etc.”
Privacy Preserving Image-Based Localization (2019, methodology): “biometric verification [76], activity recognition in videos to anonymize faces [53, 55], and detecting computer screens in first-person video [40].”
Privacy Preserving Image Queries for Camera Localization (2019, methodology): “Similar approaches were explored for image retrieval [59], face recognition [19], video surveillance [67], biometric verification [68], and detecting computer screens in firstperson video [33].”
Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images (2017, unknown): “Apart from detecting attributes, some works introduce new privacy challenges in vision such as adversarial perturbations [31, 35], privacy-preserving video capture [1, 36, 33, 37], person re-identification [2, 30], avoiding face detection [46, 16], full body re-identification [34] and privacy-sensit”