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Object Detection and Tracking With UAVs: A Framework for UAV Object Detection and Tracking Using a Thermal Imaging Camera

Leira, Frederik Stendahl
Doctoral thesis
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URI
http://hdl.handle.net/11250/2434360
Date
2017
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  • Institutt for teknisk kybernetikk [2188]
Abstract
In recent years there has been a drastic increase in the commercial availability of

small UAVs for civil and commercial applications. These applications includes,

but are not limited to, inspections of structures, search and rescue, monitoring and

surveillance of oil spills and autonomous border control. All of these applications

emphasize the UAV’s role as a remote sensing platform with the main focus on

visual sensors and data collection, and also includes an object detection and tracking

component. Object detection and tracking in UAVs using visual sensors can

generally be divided into two subproblems, where the first problem is that of controlling

the path and orientation of the UAV in such a way that the UAV is able

to capture useful data with its on-board sensors. The second problem is that of

making the UAV able to automatically detect and track objects of interest using

the UAV’s on-board sensors and autopilot. The former subproblem is considered

a problem of path planning, while the second subproblem is considered a problem

of machine vision.

This monograph is motivated by the lack of a commercial agile and adaptable object

detection and tracking framework for UAVs equipped with a visual sensor. Existing

solutions are often application specific and implemented ad hoc, resulting in

rigid object detection and tracking systems which are difficult to adapt and reconfigure

for use in other similar applications. This thesis presents a novel framework

for object detection and tracking in UAVs equipped with a visual sensor where the

focus is to make it as configurable, modularized and as hardware independent as

possible.

Chapter 2 presents a modularized object detection and tracking framework consisting

of a path planner, machine vision and object handler modules. Each module

solves a specific sub-problem of the overall object detection and tracking problem,

and each module has a wide range of applicable solutions which could be implemented

and mixed with the remainder of the system. Chapter 2 further describes

a software toolchain which provides a bridge and abstraction layer for the object

detection and tracking framework’s architecture and the UAV’s on-board hardware components. This makes the software toolchain compatible with the idea of easily

being able to configure the individual modules and hardware of the overall UAV

object detection and tracking system. This is exemplified by the implementation of

the hardware and software modules of the object detection and tracking framework

in two different UAV platforms.

In Chapter 3, a path planner module based on model predictive control (MPC)

is developed. This controller uses a kinematic model of the UAV with certain

constraints on the UAV’s motion to generate a feasible object tracking path for the

UAV. That is, the MPC assumes that the visual sensor is mounted in a gimbal with

two degrees of freedom and supplies the on-board autopilot with waypoints and

gimbal control input which enables the UAV to track an object or group of objects.

The gimbal is controlled in a way to ensure that the gimbal is pointing towards

the tracked object’s estimated position. The MPC is demonstrated through field

experiments in Chapter 3 and 5 to be able to successfully make both of the UAV

platforms developed in this thesis able to track objects.

Chapter 4 develops a solution for the machine vision module as an object detection,

recognition and tracking module compatible with the developed object detection

and tracking framework. The module enables the UAV to perform on-board image

processing of thermal images in order to automatically segment the images

into either background or objects of interest. Using the on-board telemetry data

indicating the UAVs position and attitude coupled with the segmented images, the

module is able to use a Kalman filter to track and estimate the position and velocity

of the detected objects of interest.

Chapter 5 demonstrates that the developed object detection and tracking framework

is agile and adaptable by replacing the machine vision module of the system

with a module more suitable for use in a sea ice management application. Furthermore,

the nominal tests of the alternative modules developed in this chapter show

promising results for the use of UAVs in the area of sea ice management.

A full scale test of the object detection and tracking framework where all of the

modules of the framework are activated and interacting simultaneously is still a test

that has to be conducted, although the nominal testing in Chapter 5 demonstrates

the viability of the framework and its implemented modules.
Publisher
NTNU
Series
Doctoral theses at NTNU;2017:1

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