Respectful cameras: detecting visual markers in real-time to address privacy concerns

To address privacy concerns with digital video surveillance cameras, we propose a practical, real-time approach that preserves the ability to observe actions while obscuring individual identities. In our proposed respectful cameras system, people who wish to remain anonymous agree to wear colored markers such as a hat or vest. The system automatically tracks these markers using statistical learning and classification to infer the location and size of each face and then inserts elliptical overlays. Our objective is to obscure the face of each individual wearing a marker, while minimizing the overlay area in order to maximize the remaining observable region of the scene. Our approach incorporates a visual color-tracker based on a 9 dimensional color-space by using a probabilistic AdaBoost classifier with axis-aligned hyperplanes as weak-learners. We then use particle filtering to incorporate interframe temporal information. We present experiments illustrating the performance of our system in both indoor and outdoor settings, where occlusions, multiple crossing targets, and lighting changes occur. Results suggest that the respectful camera system can reduce false negative rates to acceptable levels (under 2%).