Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Maxwell Moolchan, Senior Pranav Kumar, Freshman
Lead Presenter's Name
Maxwell Moolchan
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Bryan Watson
Abstract
Modern aerospace systems require a new approach for swarm consensus that is distributed, operates with localized information, uses simple agents, and is easily deployable and expandable. The overarching goal of our research is to advance our understanding of bed-bug behavior and use this understanding to improve performance of aerospace swarms. The central hypothesis is that if we record bed-bug response to CO2 exposure, then we will be able to improve our understanding of collective decision making because the bed bugs coordinate their response to environmental conditions. Previous research began this work by developing an algorithm capable of tracking a single target bed-bug utilizing computer vision. We seek to continue this research to develop an algorithm capable of tracking the interaction of multiple target bed-bugs at once utilizing computer vision. The research for the improved algorithm will consist of two undergraduate students and will result in the examination of six viable approaches and algorithms for tracking multiple targets utilizing computer vision. This poster is the first step of reviewing potential algorithms for selection for the project. Difficulties with our scenario include the small size of the bed bug, lack of identifying characteristics, and multiple cameras where movement needs to be tracked from one another, all of these challenges pose difficulties with many existing individual approaches.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Selection of A Computer Vision Algorithm to Track Bed Bug Swarms
Modern aerospace systems require a new approach for swarm consensus that is distributed, operates with localized information, uses simple agents, and is easily deployable and expandable. The overarching goal of our research is to advance our understanding of bed-bug behavior and use this understanding to improve performance of aerospace swarms. The central hypothesis is that if we record bed-bug response to CO2 exposure, then we will be able to improve our understanding of collective decision making because the bed bugs coordinate their response to environmental conditions. Previous research began this work by developing an algorithm capable of tracking a single target bed-bug utilizing computer vision. We seek to continue this research to develop an algorithm capable of tracking the interaction of multiple target bed-bugs at once utilizing computer vision. The research for the improved algorithm will consist of two undergraduate students and will result in the examination of six viable approaches and algorithms for tracking multiple targets utilizing computer vision. This poster is the first step of reviewing potential algorithms for selection for the project. Difficulties with our scenario include the small size of the bed bug, lack of identifying characteristics, and multiple cameras where movement needs to be tracked from one another, all of these challenges pose difficulties with many existing individual approaches.