Tracking objects and distinguishing their states by watching egocentric videos
Modi, Sahil Ketan
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https://hdl.handle.net/2142/115402
Description
Title
Tracking objects and distinguishing their states by watching egocentric videos
Author(s)
Modi, Sahil Ketan
Issue Date
2022-04-26
Director of Research (if dissertation) or Advisor (if thesis)
Gupta, Saurabh
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
computer vision
tracking
egocentric
Abstract
Interactive object understanding, or what we can do to objects and how, is a long-standing goal of computer vision. However, the inherent ambiguity of this task makes it difficult to annotate, and very few large-scale datasets exist. We realize that videos, especially egocentric ones, naturally contain this information through objects undergoing constant state changes, but learning from this data is nontrivial. Furthermore, objects are difficult to track in egocentric settings due to occlusion, drastic pose changes, and viewpoint changes. In this thesis, we propose solutions for these two challenges by (1) taking advantage of existing sparse annotations and self-supervision to achieve state-of-the-art tracking performance on TREK-150 and (2) observing human hands and their interactions with objects to learn object state-sensitive features in a self-supervised manner.
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