We propose a novel task, hierarchical instance tracking, which entails tracking all instances of predefined categories of objects and parts, while maintaining their hierarchical relationships. We introduce the first benchmark dataset supporting this task, consisting of 2,765 unique entities that are tracked in 552 videos and belong to 40 categories (across objects and parts). Evaluation of seven variants of four models tailored to our novel task reveals the new dataset is challenging. Our dataset is available at https://vizwiz.org/tasks-and-datasets/hierarchical-instance-tracking/
@article{arxiv.2512.10102,
title = {Hierarchical Instance Tracking to Balance Privacy Preservation with Accessible Information},
author = {Neelima Prasad and Jarek Reynolds and Neel Karsanbhai and Tanusree Sharma and Lotus Zhang and Abigale Stangl and Yang Wang and Leah Findlater and Danna Gurari},
journal= {arXiv preprint arXiv:2512.10102},
year = {2025}
}