This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.
Search
Skip to Search Results- 2Moving Object Detection
- 1Background Subtraction
- 1Change Detection
- 1Dynamic Background Subtraction by Generative Neural Networks
- 1Illumination invariant representation
- 1Low-rank and sparse decomposition
-
Low-rank and Sparse based Representation Methods with the Application of Moving Object Detection
DownloadFall 2019
In this thesis, we study the problem of detecting moving objects from an image sequence using low-rank and sparse representation concepts. The identification of changing or moving areas in the field of view of a camera is a fundamental step in visual surveillance, smart environments, and video...
-
Moving Object Detection Using Unsupervised and Weakly Supervised Neural Networks in Videos with Illumination Changes and Dynamic Background
DownloadFall 2023
Background subtraction is a crucial task in computer vision applications, such as video surveillance, traffic monitoring, autonomous navigation, and human-computer interaction. This approach involves acquiring a background model to separate moving objects and the background from an input image....