Undergraduate Research & Mentoring Program

Document Type

Poster

Publication Date

5-2018

Subjects

Machine learning, Drone aircraft -- Technological innovations, Automatic tracking, Drone aircraft -- Automatic control, Algorithms

Abstract

Unmanned aerial vehicles, also known as drones, have been more and more widely used in recent decades because of their mobility. They appear in many applications such as farming, search and rescue, entertainment, military, and so on. Such high demands for drones lead to the need of developments in drone technologies. Next generations of commercial and military drones are expected to be aware of surrounding objects while flying autonomously in different terrains and conditions. One of the biggest challenges to drone automation is the ability to detect and track objects of interest in real-time. While there are many robust machine learning algorithms for object detection and tracking, these algorithms may not perform as expected on drones due to low computing power system. Furthermore, attaching additional computing power or hardware to drones is not feasible due to weight constraints. We aim to implement machine learning algorithms for the drones to perform real-time object detection and tracking using on-board camera and low-power embedded system. We propose a handshake mechanism between object detection and object tracking which takes advantage of object tracking algorithm’s run-time and object detection algorithm’s accuracy.

Persistent Identifier

http://archives.pdx.edu/ds/psu/25187

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