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.
@article{arxiv.1809.10242,
title = {Addressing Training Bias via Automated Image Annotation},
author = {Zhujun Xiao and Yanzi Zhu and Yuxin Chen and Ben Y. Zhao and Junchen Jiang and Haitao Zheng},
journal= {arXiv preprint arXiv:1809.10242},
year = {2018}
}