Detection of multiple-lane markings (lane-line) on road surfaces is an essential aspect
of autonomous vehicles. Although several approaches have been proposed to detect
lanes, detecting multiple lane-lines consistently, particularly across a stream of frames
and under varying lighting conditions is still a challenging problem. Since the road's
markings are designed to be smooth and parallel, lane-line sampled features tend
to be spatially and temporally correlated inside and between frames. In this thesis,
we develop novel methods to model these spatial and temporal dependencies in the
form of the target tracking problem. In fact, instead of resorting to the conventional
method of processing each frame to detect lanes only in the space domain, we treat
the overall problem as a Multiple Extended Target Tracking (METT) problem.
In the first step, we modelled lane-lines as multiple "independent" extended targets
and developed a spline mathematical model for the shape of the targets. We showed
that expanding the estimations across the time domain could improve the result of
estimation. We identify a set of control points for each spline, which will track over
time. To overcome the clutter problem, we developed an integrated probabilistic data
association fi lter (IPDAF) as our basis, and formulated a METT algorithm to track
multiple splines corresponding to each lane-line.In the second part of our work, we investigated the coupling between multiple extended targets. We considered the non-parametric case and modeled target dependency
using the Multi-Output Gaussian Process. We showed that considering
dependency between extended targets could improve shape estimation results. We
exploit the dependency between extended targets by proposing a novel recursive approach
called the Multi-Output Spatio-Temporal Gaussian Process Kalman Filter
(MO-STGP-KF). We used MO-STGP-KF to estimate and track multiple dependent
lane markings that are possibly degraded or obscured by traffic. Our method tested
for tracking multiple lane-lines but can be employed to track multiple dependent
rigid-shape targets by using the measurement model in the radial space
In the third section, we developed a Spatio-Temporal Joint Probabilistic Data
Association Filter (ST-JPDAF). In multiple extended target tracking problems with
clutter, sometimes extended targets share measurements: for example, in lane-line
detection, when two-lane markings pass or merge together. In single-point target
tracking, this problem can be solved using the famous Joint Probabilistic Data Association
(JPDA) filter. In the single-point case, even when measurements are dependent,
we can stack them in the coupled form of JPDA. In this last chapter, we expanded
JPDA for tracking multiple dependent extended targets using an approach called
ST-JPDAF. We managed dependency of measurements in space (inside a frame) and
time (between frames) using different kernel functions, which can be learned using
the trained data. This extension can be used to track the shape and dynamic of
dependent extended targets within clutter when targets share measurements.
The performance of the proposed methods in all three chapters are quanti ed on
real data scenarios and their results are compared against well-known model-based,
semi-supervised, and fully-supervised methods. The proposed methods offer very promising results. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26254 |
| Date | January 2021 |
| Creators | akbari, behzad |
| Contributors | Kirubarajan, Thia, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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