Given the lack of modern techniques to ensure the digital privacy of individuals, we want to pave the way for a new approach to make pedestrians in cityscape images anonymous. To address these concerns, we propose an automated method to replace any unknown pedestrian with another one which is extracted from a controlled and authorized dataset. The techniques used up to now to make people anonymous are based mainly on the blurring of people's faces, but even so it is possible to trace the identity of the subject starting from his clothing, personal items, hairstyle, the place and time where the photo was taken. The proposed method aims to make the pedestrians completely anonymous, and consists of four phases: firstly we identify the area where the pedestrian is located, we separate the pedestrian from the background, we select the most similar pedestrian from a controlled dataset and subsequently we substitute it. Our case study is Google Street View because it is one of the online services which suffers most from this kind of privacy issues. The experimental results show how this technique can overcome the problems of digital privacy with promising results.
Digital privacy: Replacing pedestrians from Google Street View images
GALLO, IGNAZIO
2012-01-01
Abstract
Given the lack of modern techniques to ensure the digital privacy of individuals, we want to pave the way for a new approach to make pedestrians in cityscape images anonymous. To address these concerns, we propose an automated method to replace any unknown pedestrian with another one which is extracted from a controlled and authorized dataset. The techniques used up to now to make people anonymous are based mainly on the blurring of people's faces, but even so it is possible to trace the identity of the subject starting from his clothing, personal items, hairstyle, the place and time where the photo was taken. The proposed method aims to make the pedestrians completely anonymous, and consists of four phases: firstly we identify the area where the pedestrian is located, we separate the pedestrian from the background, we select the most similar pedestrian from a controlled dataset and subsequently we substitute it. Our case study is Google Street View because it is one of the online services which suffers most from this kind of privacy issues. The experimental results show how this technique can overcome the problems of digital privacy with promising results.
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