Strack: Secure Tracking In Community Surveillance

We present sTrack, a system that can track objects across multiple cameras without sharing any visual information between two cameras except whether an object was seen by both. To achieve this challenging privacy goal, we leverage recent advances in secure two-party computation and multi-camera tracking. We derive a new distance metric learning technique that is more suited for secure computation. Compared to the existing methods, our technique has lower complexity in secure computation without sacrificing the tracking accuracy. We implement it using a new Boolean circuit for secure tracking. Experiments using real datasets show that the performance overhead of secure tracking is low, adding only a few seconds over non-private tracking.