UC San Diego

Object detection and segmentation are some of the key components of Computer Vision, which have wide ranging real world applications. The current state of the art techniques in computer vision are based on Deep Neural Networks and one of the key challenges is using the state of the art techniques in these fields on novel images, and videos in different environments, and classes. These methods require expensive manual annotations and transfer learning to make them work on domains different from their training data sets. In this thesis, we explore both domain adaptation, and deep learning techniques that don’t necessarily rely on the idea of a class, to help with the annotation of private videos. We implemented the initial idea of domain adaptation for directly annotating objects and followed with using video object segmentation (VOS) tracking methods for propagating annotations. Their application to a novel video acquired in the GURU lab is explored as well as ways to improve their performance.