  Build accurate DNN models requires training on large labeled, context
specific datasets, especially those matching the target scenario. We believe
advances in wireless localization, working in unison with cameras, can produce
automated annotation of targets on images and videos captured in the wild.
Using pedestrian and vehicle detection as examples, we demonstrate the
feasibility, benefits, and challenges of an automatic image annotation system.
Our work calls for new technical development on passive localization, mobile
data analytics, and error-resilient ML models, as well as design issues in user
privacy policies.
