Abstract
Cameras are now commonplace in our social and computing landscapes and embedded into consumer devices like smartphones and tablets. A new generation of wearable devices (such as Google Glass) will soon make 'first-person' cameras nearly ubiquitous, capturing vast amounts of imagery without deliberate human action. `Lifelogging' devices and applications will record and share images from people's daily lives with their social networks. These devices that automatically capture images in the background raise serious privacy concerns, since they are likely to capture deeply private information. Users of these devices need ways to identify and prevent the sharing of sensitive images.
As a first step, we introduce PlaceAvoider, a technique for owners of first-person cameras to `blacklist' sensitive spaces (like bathrooms and bedrooms). PlaceAvoider recognizes images captured in these spaces and flags them for review before the images are made available to applications. PlaceAvoider performs novel image analysis using both fine-grained image features (like specific objects) and coarse-grained, scene-level features (like colors and textures) to classify where a photo was taken. PlaceAvoider combines these features in a probabilistic framework that jointly labels streams of images in order to improve accuracy. We test the technique on five realistic first-person image datasets and show it is robust to blurriness, motion, and occlusion.