| Records |
| Author |
Joan Oliver; Ricardo Toledo; J. Pujol; J. Sorribes; E. Valderrama |
| Title |
Un ABP basado en la robotica para las ingenierias informaticas |
Type |
Miscellaneous |
| Year |
2009 |
Publication |
15th Jornadas de Enseñanza Universitaria de la Informatica |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
331–338 |
| Keywords |
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| Abstract |
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| Address |
Barcelona, Spain |
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Edition |
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ISBN |
ISBN:978–84–692–2758–9 |
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Expedition |
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Conference |
JENUI |
Notes  |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ OTP2009 |
Serial |
1253 |
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| Author |
Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez |
| Title |
Video alignment for automotive applications |
Type |
Miscellaneous |
| Year |
2009 |
Publication |
BMVA one–day technical meeting on vision for automotive applications |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
video alignment |
| Abstract |
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| Address |
London, UK |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ DPS2009 |
Serial |
1271 |
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| Author |
Jose Manuel Alvarez; Antonio Lopez |
| Title |
Model-based road detection using shadowless features and on-line learning |
Type |
Miscellaneous |
| Year |
2009 |
Publication |
BMVA one–day technical meeting on vision for automotive applications |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
road detection |
| Abstract |
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| Address |
London, UK |
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Expedition |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ AlA2009 |
Serial |
1272 |
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| Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
| Title |
An Iterative Multiresolution Scheme for SFM with Missing Data: single and multiple object scenes |
Type |
Journal Article |
| Year |
2010 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
| Volume |
28 |
Issue |
1 |
Pages |
164-176 |
| Keywords |
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| Abstract |
Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach. |
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Edition |
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| ISSN |
0262-8856 |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ JSL2010 |
Serial |
1278 |
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| Author |
David Geronimo |
| Title |
A Global Approach to Vision-Based Pedestrian Detection for Advanced Driver Assistance Systems |
Type |
Book Whole |
| Year |
2010 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
At the beginning of the 21th century, traffic accidents have become a major problem not only for developed countries but also for emerging ones. As in other scientific areas in which Artificial Intelligence is becoming a key actor, advanced driver assistance systems, and concretely pedestrian protection systems based on Computer Vision, are becoming a strong topic of research aimed at improving the safety of pedestrians. However, the challenge is of considerable complexity due to the varying appearance of humans (e.g., clothes, size, aspect ratio, shape, etc.), the dynamic nature of on-board systems and the unstructured moving environments that urban scenarios represent. In addition, the required performance is demanding both in terms of computational time and detection rates. In this thesis, instead of focusing on improving specific tasks as it is frequent in the literature, we present a global approach to the problem. Such a global overview starts by the proposal of a generic architecture to be used as a framework both to review the literature and to organize the studied techniques along the thesis. We then focus the research on tasks such as foreground segmentation, object classification and refinement following a general viewpoint and exploring aspects that are not usually analyzed. In order to perform the experiments, we also present a novel pedestrian dataset that consists of three subsets, each one addressed to the evaluation of a different specific task in the system. The results presented in this thesis not only end with a proposal of a pedestrian detection system but also go one step beyond by pointing out new insights, formalizing existing and proposed algorithms, introducing new techniques and evaluating their performance, which we hope will provide new foundations for future research in the area. |
| Address |
Antonio Lopez;Krystian Mikolajczyk;Jaume Amores;Dariu M. Gavrila;Oriol Pujol;Felipe Lumbreras |
| Corporate Author |
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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Antonio Lopez;Krystian Mikolajczyk;Jaume Amores;Dariu M. Gavrila;Oriol Pujol;Felipe Lumbreras |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-84-936529-5-1 |
Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ Ger2010 |
Serial |
1279 |
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| Author |
Jaume Amores |
| Title |
Vocabulary-based Approaches for Multiple-Instance Data: a Comparative Study |
Type |
Conference Article |
| Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
4246–4250 |
| Keywords |
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| Abstract |
Multiple Instance Learning (MIL) has become a hot topic and many different algorithms have been proposed in the last years. Despite this fact, there is a lack of comparative studies that shed light into the characteristics of the different methods and their behavior in different scenarios. In this paper we provide such an analysis. We include methods from different families, and pay special attention to vocabulary-based approaches, a new family of methods that has not received much attention in the MIL literature. The empirical comparison includes seven databases from four heterogeneous domains, implementations of eight popular MIL methods, and a study of the behavior under synthetic conditions. Based on this analysis, we show that, with an appropriate implementation, vocabulary-based approaches outperform other MIL methods in most of the cases, showing in general a more consistent performance. |
| Address |
Istanbul, Turkey |
| Corporate Author |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ Amo2010 |
Serial |
1295 |
| Permanent link to this record |
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| Author |
Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras; T. Graf |
| Title |
Robust lane markings detection and road geometry computation |
Type |
Journal Article |
| Year |
2010 |
Publication |
International Journal of Automotive Technology |
Abbreviated Journal |
IJAT |
| Volume |
11 |
Issue |
3 |
Pages |
395–407 |
| Keywords |
lane markings |
| Abstract |
Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
The Korean Society of Automotive Engineers |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1229-9138 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ LSC2010 |
Serial |
1300 |
| Permanent link to this record |
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| Author |
David Geronimo; Angel Sappa; Antonio Lopez |
| Title |
Stereo-based Candidate Generation for Pedestrian Protection Systems |
Type |
Book Chapter |
| Year |
2010 |
Publication |
Binocular Vision: Development, Depth Perception and Disorders |
Abbreviated Journal |
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| Volume |
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Issue |
9 |
Pages |
189–208 |
| Keywords |
Pedestrian Detection |
| Abstract |
This chapter describes a stereo-based algorithm that provides candidate image windows to a latter 2D classification stage in an on-board pedestrian detection system. The proposed algorithm, which consists of three stages, is based on the use of both stereo imaging and scene prior knowledge (i.e., pedestrians are on the ground) to reduce the candidate searching space. First, a successful road surface fitting algorithm provides estimates on the relative ground-camera pose. This stage directs the search toward the road area thus avoiding irrelevant regions like the sky. Then, three different schemes are used to scan the estimated road surface with pedestrian-sized windows: (a) uniformly distributed through the road surface (3D); (b) uniformly distributed through the image (2D); (c) not uniformly distributed but according to a quadratic function (combined 2D-3D). Finally, the set of candidate windows is reduced by analyzing their 3D content. Experimental results of the proposed algorithm, together with statistics of searching space reduction are provided. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
NOVA Publishers |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ GSL2010 |
Serial |
1301 |
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| Author |
Mohammad Rouhani; Angel Sappa |
| Title |
Relaxing the 3L Algorithm for an Accurate Implicit Polynomial Fitting |
Type |
Conference Article |
| Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3066-3072 |
| Keywords |
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| Abstract |
This paper presents a novel method to increase the accuracy of linear fitting of implicit polynomials. The proposed method is based on the 3L algorithm philosophy. The novelty lies on the relaxation of the additional constraints, already imposed by the 3L algorithm. Hence, the accuracy of the final solution is increased due to the proper adjustment of the expected values in the aforementioned additional constraints. Although iterative, the proposed approach solves the fitting problem within a linear framework, which is independent of the threshold tuning. Experimental results, both in 2D and 3D, showing improvements in the accuracy of the fitting are presented. Comparisons with both state of the art algorithms and a geometric based one (non-linear fitting), which is used as a ground truth, are provided. |
| Address |
San Francisco; CA; USA; June 2010 |
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Place of Publication |
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Summary Language |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ RoS2010a |
Serial |
1303 |
| Permanent link to this record |
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| Author |
Javier Marin; David Vazquez; David Geronimo; Antonio Lopez |
| Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
Type |
Conference Article |
| Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
137–144 |
| Keywords |
Pedestrian Detection; Domain Adaptation |
| Abstract |
Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance. |
| Address |
San Francisco; CA; USA; June 2010 |
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Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
English |
Original Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ MVG2010 |
Serial |
1304 |
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| Author |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
| Title |
Fast and Robust Object Segmentation with the Integral Linear Classifier |
Type |
Conference Article |
| Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1046–1053 |
| Keywords |
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| Abstract |
We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets. |
| Address |
San Francisco; CA; USA; June 2010 |
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Place of Publication |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ ARL2010a |
Serial |
1311 |
| Permanent link to this record |
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| Author |
David Geronimo; Antonio Lopez; Angel Sappa; Thorsten Graf |
| Title |
Survey on Pedestrian Detection for Advanced Driver Assistance Systems |
Type |
Journal Article |
| Year |
2010 |
Publication |
IEEE Transaction on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
32 |
Issue |
7 |
Pages |
1239–1258 |
| Keywords |
ADAS, pedestrian detection, on-board vision, survey |
| Abstract |
Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges. |
| Address |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0162-8828 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ GLS2010 |
Serial |
1340 |
| Permanent link to this record |
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| Author |
David Geronimo; Angel Sappa; Daniel Ponsa; Antonio Lopez |
| Title |
2D-3D based on-board pedestrian detection system |
Type |
Journal Article |
| Year |
2010 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
114 |
Issue |
5 |
Pages |
583–595 |
| Keywords |
Pedestrian detection; Advanced Driver Assistance Systems; Horizon line; Haar wavelets; Edge orientation histograms |
| Abstract |
During the next decade, on-board pedestrian detection systems will play a key role in the challenge of increasing traffic safety. The main target of these systems, to detect pedestrians in urban scenarios, implies overcoming difficulties like processing outdoor scenes from a mobile platform and searching for aspect-changing objects in cluttered environments. This makes such systems combine techniques in the state-of-the-art Computer Vision. In this paper we present a three module system based on both 2D and 3D cues. The first module uses 3D information to estimate the road plane parameters and thus select a coherent set of regions of interest (ROIs) to be further analyzed. The second module uses Real AdaBoost and a combined set of Haar wavelets and edge orientation histograms to classify the incoming ROIs as pedestrian or non-pedestrian. The final module loops again with the 3D cue in order to verify the classified ROIs and with the 2D in order to refine the final results. According to the results, the integration of the proposed techniques gives rise to a promising system. |
| Address |
Computer Vision and Image Understanding (Special Issue on Intelligent Vision Systems), Vol. 114(5):583-595 |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1077-3142 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ GSP2010 |
Serial |
1341 |
| Permanent link to this record |
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| Author |
Naveen Onkarappa; Angel Sappa |
| Title |
On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow |
Type |
Conference Article |
| Year |
2010 |
Publication |
7th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
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| Volume |
6111 |
Issue |
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Pages |
230-239 |
| Keywords |
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| Abstract |
This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach. |
| Address |
Povoa de Varzim (Portugal) |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-13771-6 |
Medium |
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| Area |
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Expedition |
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Conference |
ICIAR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ OnS2010 |
Serial |
1342 |
| Permanent link to this record |
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| Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
| Title |
Multimodal Template Matching based on Gradient and Mutual Information using Scale-Space |
Type |
Conference Article |
| Year |
2010 |
Publication |
17th IEEE International Conference on Image Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2749–2752 |
| Keywords |
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| Abstract |
This paper presents the combined use of gradient and mutual information for infrared and intensity templates matching. We propose to joint: (i) feature matching in a multiresolution context and (ii) information propagation through scale-space representations. Our method consists in combining mutual information with a shape descriptor based on gradient, and propagate them following a coarse-to-fine strategy. The main contributions of this work are: to offer a theoretical formulation towards a multimodal stereo matching; to show that gradient and mutual information can be reinforced while they are propagated between consecutive levels; and to show that they are valid cost functions in multimodal template matchings. Comparisons are presented showing the improvements and viability of the proposed approach. |
| Address |
Hong-Kong |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
1522-4880 |
ISBN |
978-1-4244-7992-4 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
ICIP |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ BLS2010 |
Serial |
1358 |
| Permanent link to this record |
