Data-Driven Adaptive Learning for Video Analytics
Abstract
We will apply the methods of the Dynamic Data Drive Application learning (DDAL) framework on applications for the detection of people, and objects, such as vehicles, buildings, etc., and the understanding of complex scenes for change detection. These applications will involve a physical robotic testbed that will directly integrate the adaptive learning and dynamic control to thoroughly evaluate our DDAL framework.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 09, 2018
- Source ID
- FA95501810121
Entities
People
- Andreas Savakis
Organizations
- Air Force Office of Scientific Research
- Rochester Institute of Technology
- United States Air Force