Graduate Thesis Or Dissertation

Video Analysis : Techniques for Semi-Supervised Video Object Instance Segmentation and Tracking-by-Detection in the Wild

Public Deposited

Downloadable Content

Download PDF


Attribute NameValues
  • This thesis consists of two major components. The first part is concerned with video object instance segmentation (VOS), which is the task of assigning per-pixel labels perframe of a video sequence to indicate foreground object instance membership, given the first frame ground truth mask. VOS has myriad applications, from video post-processing to action recognition, and is an active area of research. A novel end-to-end trainable, online algorithm utilizing a bilinear LSTM to learn long-term appearance models is presented. The bilinear LSTM is used to guide the learned CNN features, integrating temporal information and building more discriminative appearance features for specific objects during inference. The second part of this thesis examines computer vision's potential applications for performing automated ecological inference for endemic flat-fish populations. Specifically, it looks at the construction of a visual tracking dataset, NHFish, consisting of underwater beam trawl videos collected along the Newport Hydrographic Line of Oregon coast benthos and the application of automated methods for video analysis of the beam trawl videos.
Resource Type
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Academic Affiliation
Rights Statement
Peer Reviewed
Embargo reason
  • Ongoing Research
Embargo date range
  • 2018-07-23 to 2019-02-22



This work has no parents.

In Collection: