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Author  |
Akhil Gurram |
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Title |
Monocular Depth Estimation for Autonomous Driving |
Type |
Book Whole |
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Year |
2022 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
3D geometric information is essential for on-board perception in autonomous driving and driver assistance. Autonomous vehicles (AVs) are equipped with calibrated sensor suites. As part of these suites, we can find LiDARs, which are expensive active sensors in charge of providing the 3D geometric information. Depending on the operational conditions for the AV, calibrated stereo rigs may be also sufficient for obtaining 3D geometric information, being these rigs less expensive and easier to install than LiDARs. However, ensuring a proper maintenance and calibration of these types of sensors is not trivial. Accordingly, there is an increasing interest on performing monocular depth estimation (MDE) to obtain 3D geometric information on-board. MDE is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Moreover, a set of single cameras with MDE capabilities would still be a cheap solution for on-board perception, relatively easy to integrate and maintain in an AV.
Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Accordingly, the overall goal of this PhD is to study methods for improving CNN-based MDE accuracy under different training settings. More specifically, this PhD addresses different research questions that are described below. When we started to work in this PhD, state-of-theart methods for MDE were already based on CNNs. In fact, a promising line of work consisted in using image-based semantic supervision (i.e., pixel-level class labels) while training CNNs for MDE using LiDAR-based supervision (i.e., depth). It was common practice to assume that the same raw training data are complemented by both types of supervision, i.e., with depth and semantic labels. However, in practice, it was more common to find heterogeneous datasets with either only depth supervision or only semantic supervision. Therefore, our first work was to research if we could train CNNs for MDE by leveraging depth and semantic information from heterogeneous datasets. We show that this is indeed possible, and we surpassed the state-of-the-art results on MDE at the time we did this research. To achieve our results, we proposed a particular CNN architecture and a new training protocol.
After this research, it was clear that the upper-bound setting to train CNN-based MDE models consists in using LiDAR data as supervision. However, it would be cheaper and more scalable if we would be able to train such models from monocular sequences. Obviously, this is far more challenging, but worth to research. Training MDE models using monocular sequences is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. To alleviate these problems, we perform MDE by virtual-world supervision and real-world SfM self-supervision. We call our proposalMonoDEVSNet. We compensate the SfM self-supervision limitations by leveraging
virtual-world images with accurate semantic and depth supervision, as well as addressing the virtual-to-real domain gap. MonoDEVSNet outperformed previous MDE CNNs trained on monocular and even stereo sequences. We have publicly released MonoDEVSNet at <https://github.com/HMRC-AEL/MonoDEVSNet>.
Finally, since MDE is performed to produce 3D information for being used in downstream tasks related to on-board perception. We also address the question of whether the standard metrics for MDE assessment are a good indicator for future MDE-based driving-related perception tasks. By using 3D object detection on point clouds as proxy of on-board perception, we conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods which reflects relatively well the 3D object detection results we may expect. |
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Address |
March, 2022 |
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Corporate Author |
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Thesis |
Ph.D. thesis |
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Publisher |
IMPRIMA |
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Editor |
Antonio Lopez;Onay Urfalioglu |
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ISBN |
978-84-124793-0-0 |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ Gur2022 |
Serial |
3712 |
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Author |
Alejandro Gonzalez Alzate |
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Title |
Multi-modal Pedestrian Detection |
Type |
Book Whole |
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Year |
2015 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
Pedestrian detection continues to be an extremely challenging problem in real scenarios, in which situations like illumination changes, noisy images, unexpected objects, uncontrolled scenarios and variant appearance of objects occur constantly. All these problems force the development of more robust detectors for relevant applications like vision-based autonomous vehicles, intelligent surveillance, and pedestrian tracking for behavior analysis. Most reliable vision-based pedestrian detectors base their decision on features extracted using a single sensor capturing complementary features, e.g., appearance, and texture. These features usually are extracted from the current frame, ignoring temporal information, or including it in a post process step e.g., tracking or temporal coherence. Taking into account these issues we formulate the following question: can we generate more robust pedestrian detectors by introducing new information sources in the feature extraction step?
In order to answer this question we develop different approaches for introducing new information sources to well-known pedestrian detectors. We start by the inclusion of temporal information following the Stacked Sequential Learning (SSL) paradigm which suggests that information extracted from the neighboring samples in a sequence can improve the accuracy of a base classifier.
We then focus on the inclusion of complementary information from different sensors like 3D point clouds (LIDAR – depth), far infrared images (FIR), or disparity maps (stereo pair cameras). For this end we develop a multi-modal framework in which information from different sensors is used for increasing detection accuracy (by increasing information redundancy). Finally we propose a multi-view pedestrian detector, this multi-view approach splits the detection problem in n sub-problems.
Each sub-problem will detect objects in a given specific view reducing in that way the variability problem faced when a single detectors is used for the whole problem. We show that these approaches obtain competitive results with other state-of-the-art methods but instead of design new features, we reuse existing ones boosting their performance. |
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Address |
November 2015 |
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Corporate Author |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
David Vazquez;Antonio Lopez; |
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ISBN |
978-84-943427-7-6 |
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Notes |
ADAS; 600.076 |
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no |
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Call Number |
Admin @ si @ Gon2015 |
Serial |
2706 |
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Author |
Alicia Fornes; Gemma Sanchez |
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Title |
Analysis and Recognition of Music Scores |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
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Volume |
E |
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Pages |
749-774 |
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Abstract |
The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented. |
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Publisher |
Springer London |
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Editor |
D. Doermann; K. Tombre |
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ISBN |
978-0-85729-860-7 |
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Notes |
DAG; ADAS; 600.076; 600.077 |
Approved |
no |
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Call Number |
Admin @ si @ FoS2014 |
Serial |
2484 |
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Author |
Angel Sappa (ed) |
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Title |
Computer Graphics and Imaging |
Type |
Book Whole |
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Year |
2010 |
Publication |
Computer Graphics and Imaging |
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Editor |
Angel Sappa |
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978–0–88986–836–6 |
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CGIM |
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Notes |
ADAS |
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no |
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Call Number |
ADAS @ adas @ Sap2010 |
Serial |
1468 |
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Author |
Angel Sappa; Boris X. Vintimilla |
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Title |
Edge Point Linking by Means of Global and Local Schemes |
Type |
Book Chapter |
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Year |
2008 |
Publication |
in Signal Processing for Image Enhancement and Multimedia Processing |
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11 |
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Pages |
115–125 |
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Springer |
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Editor |
E. Damiani |
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ADAS |
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Call Number |
ADAS @ adas @ SaV2008 |
Serial |
938 |
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Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez |
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Title |
Stereo Vision Camera Pose Estimation for On-Board Applications |
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Book Chapter |
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Year |
2007 |
Publication |
Scene Reconstruction, Pose Estimation and Traking |
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Pages |
39-50 |
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Publisher |
Rustam Stolking |
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978-3-902613-06-6 |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ SGD2007 |
Serial |
797 |
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Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Mohammad Rouhani; Antonio Lopez |
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Title |
Moving object detection from mobile platforms using stereo data registration |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Computational Intelligence paradigms in advanced pattern classification |
Abbreviated Journal |
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Volume |
386 |
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Pages |
25-37 |
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Keywords |
pedestrian detection |
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Abstract |
This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios. |
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Springer Berlin Heidelberg |
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Editor |
Marek R. Ogiela; Lakhmi C. Jain |
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ISSN |
1860-949X |
ISBN |
978-3-642-24048-5 |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ SGD2012 |
Serial |
2061 |
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Author |
Angel Sappa; Fadi Dornaika |
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Title |
An Edge-Based Approach to Motion Detection |
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Book Chapter |
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Year |
2006 |
Publication |
6th International Conference on Computational Science (ICCS´06), LNCS 3991:563–570 |
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Reading (United Kingdom) |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ SaD2006 |
Serial |
654 |
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Author |
Angel Sappa; George A. Triantafyllid |
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Title |
Computer Graphics and Imaging |
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2012 |
Publication |
Computer Graphics and Imaging |
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Crete, Greece |
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978-0-88986-921-9 |
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ADAS |
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no |
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Call Number |
Admin @ si @ Sap2012 |
Serial |
2067 |
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Author |
Angel Sappa; Jordi Vitria |
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Title |
Multimodal Interaction in Image and Video Applications |
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Book Whole |
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Year |
2013 |
Publication |
Multimodal Interaction in Image and Video Applications |
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48 |
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Abstract |
Book Series Intelligent Systems Reference Library |
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Springer Berlin Heidelberg |
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1868-4394 |
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978-3-642-35931-6 |
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ADAS; OR;MV |
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Call Number |
Admin @ si @ SaV2013 |
Serial |
2199 |
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