A Framework for Fair and Trustworthy Multimodal Video Anomaly Detection: Fusion, Dataset Quality Auditing, Explainability, and Fairness

Author

Omeshamisu Judith Anigala, South Dakota State UniversityFollow

Document Type

Dissertation - Open Access

Award Date

2025

Degree Name

Doctor of Philosophy (PhD)

Department / School

Electrical Engineering and Computer Science

First Advisor

Chulwoo Pack

Library of Congress Subject Headings

Anomaly detection (Computer security) 
Computer vision. 
Video surveillance. 
Multimodal user interfaces (Computer systems)      

Publisher

South Dakota State University

Recommended Citation

Anigala, Omeshamisu Judith, "A Framework for Fair and Trustworthy Multimodal Video Anomaly Detection: Fusion, Dataset Quality Auditing, Explainability, and Fairness" (2025). Electronic Theses and Dissertations. 1887.
https://openprairie.sdstate.edu/etd2/1887