Hierarchical Instance Tracking to Balance Privacy Preservation with Accessible Information
Neelima Prasad ⋅ Jarek Reynolds ⋅ Neel Karsanbhai ⋅ Tanusree Sharma ⋅ Lotus Zhang ⋅ Abigale Stangl ⋅ Yang Wang ⋅ Leah Findlater ⋅ Danna Gurari
Abstract
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. To facilitate community-wide progress, we publicly release our dataset with an evaluation server at https://anonymous.com.
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