eprintid: 23722
rev_number: 12
eprint_status: archive
userid: 3427
dir: disk0/00/02/37/22
datestamp: 2017-11-23 10:24:27
lastmod: 2017-12-21 10:28:10
status_changed: 2017-11-23 10:24:27
type: doctoralThesis
metadata_visibility: show
creators_name: Neufeld, Andreas
title: Variational Approaches for Motion and Structure from Monocular Video
subjects: ddc-004
subjects: ddc-510
divisions: i-110400
adv_faculty: af-11
abstract: Motion and structure estimation are elementary problems of computer vision. These are active areas of research, even though the first methods were proposed several decades ago. We develop new approaches for motion and structure estimation for autonomous driving. An autonomous vehicle requires an accurate model of its environment, wrong decisions made by an autonomous car can have severe consequences. We assume the monocular setup, where only a single camera is mounted on the car.
Outdoor traffic sequences are challenging for optical flow estimation. The high speed of the car causes large displacements in the optical flow field, the lighting conditions are unstable and there can be strong
distortions due to reflections and difficult weather conditions. We propose new discrete methods, which determine optical flow as optimal configuration of probabilistic graphical models. 
The first approach selects sparse locations in the reference frame, and matches them across the second image. The best correspondences, which match constraints from a multiple view configuration, are considered motion vectors in a graphical model. In a second approach, we solve for dense optical flow by approximating the original infeasible graphical model with a sequence of reduced models.
The monocular configuration poses challenges to the estimation of scene structure, camera positions and scene parameters need to be estimated jointly. The geometry of multiple views creates blind spots in the images, and adds a scale ambiguity, which both to not exist in a setup with multiple cameras. 
We propose two methods for structure estimation. The first approach determines the energy optimal camera track, given optical flow and depth observations. A further approach estimates camera motion and a piecewise planar scene description jointly from a single optical
flow field. The scene description contains depth and plane normal information. 
We evaluate our approaches for motion and structure estimation on different real world and rendered datasets. In addition to evaluation on publicly available evaluation data, we evaluate on a new rendered dataset with ground truth plane normals.
date: 2017
id_scheme: DOI
id_number: 10.11588/heidok.00023722
ppn_swb: 1657505308
own_urn: urn:nbn:de:bsz:16-heidok-237225
date_accepted: 2017-11-07
advisor: HASH(0x55d64be9ff58)
language: eng
bibsort: NEUFELDANDVARIATIONA2017
full_text_status: public
citation:   Neufeld, Andreas  (2017) Variational Approaches for Motion and Structure from Monocular Video.  [Dissertation]     
document_url: http://archiv.ub.uni-heidelberg.de/volltextserver/23722/1/Thesis_Neufeld.pdf