Eye-Tracking based Object Detection using Drone Captured Video Streams
Description/Abstract/Artist Statement
The use of unmanned aerial vehicles (UAVs) or drones, has significantly increased over the past few years. There is a growing demand in the drone industry, creating new workforce opportunities such as package delivery, search and rescue, real estate, transportation, agriculture, infrastructure inspection, and many others, signifying the importance of effective and efficient control techniques. We propose a scheme for controlling a drone through gaze extracted from eye-trackers, enabling an operator to focus a drone on an object in a scene. Using the object detection model You Only Look Once (YOLO), our plan is to have a user identify an object in a video feed. As the user watches the drone’s video feed on a screen, different objects are identified by YOLO in the feed. The user will then identify and focus on an object in the video feed. Once the object is selected and identified, the drone will begin to track that object.
Faculty Advisor/Mentor
Sampath Jayarathna
Faculty Advisor/Mentor Department
Computer Science Department
College Affiliation
College of Sciences
Presentation Type
Poster
Disciplines
Artificial Intelligence and Robotics | Data Science | Graphics and Human Computer Interfaces
Session Title
Poster Session
Location
Learning Commons Lobby @ Perry Library
Start Date
3-25-2023 8:30 AM
End Date
3-25-2023 10:00 AM
Eye-Tracking based Object Detection using Drone Captured Video Streams
Learning Commons Lobby @ Perry Library
The use of unmanned aerial vehicles (UAVs) or drones, has significantly increased over the past few years. There is a growing demand in the drone industry, creating new workforce opportunities such as package delivery, search and rescue, real estate, transportation, agriculture, infrastructure inspection, and many others, signifying the importance of effective and efficient control techniques. We propose a scheme for controlling a drone through gaze extracted from eye-trackers, enabling an operator to focus a drone on an object in a scene. Using the object detection model You Only Look Once (YOLO), our plan is to have a user identify an object in a video feed. As the user watches the drone’s video feed on a screen, different objects are identified by YOLO in the feed. The user will then identify and focus on an object in the video feed. Once the object is selected and identified, the drone will begin to track that object.