| |
Records |
Links |
|
Author |
Hugo Berti; Angel Sappa; Osvaldo Agamennoni |
|
| |
Title |
Autonomous robot navigation with a global and asymptotic convergence |
Type |
Conference Article |
| |
Year |
2007 |
Publication |
IEEE International Conference on Robotics and Automation |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2712–2717 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address  |
Roma (Italy) |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICRA |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ BSA2007 |
Serial |
796 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat |
|
| |
Title |
Photometric Stereo through and Adapted Alternation Approach |
Type |
Conference Article |
| |
Year |
2008 |
Publication |
IEEE International Conference on Image Processing, |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1500–1503 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
San Diego; CA; USA |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ JSL2008d |
Serial |
1016 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jose Manuel Alvarez; Felipe Lumbreras; Theo Gevers; Antonio Lopez |
|
| |
Title |
Geographic Information for vision-based Road Detection |
Type |
Conference Article |
| |
Year |
2010 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
621–626 |
|
| |
Keywords |
road detection |
|
| |
Abstract |
Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach. |
|
| |
Address |
San Diego; CA; USA |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
IV |
|
| |
Notes |
ADAS;ISE |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ ALG2010 |
Serial |
1428 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez |
|
| |
Title |
Embedded real-time stereo estimation via Semi-Global Matching on the GPU |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
16th International Conference on Computational Science |
Abbreviated Journal |
|
|
| |
Volume |
80 |
Issue |
|
Pages |
143-153 |
|
| |
Keywords |
Autonomous Driving; Stereo; CUDA; 3d reconstruction |
|
| |
Abstract |
Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method. |
|
| |
Address |
San Diego; CA; USA; June 2016 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCS |
|
| |
Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ HCE2016a |
Serial |
2740 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Victor Campmany; Sergio Silva; Antonio Espinosa; Juan Carlos Moure; David Vazquez; Antonio Lopez |
|
| |
Title |
GPU-based pedestrian detection for autonomous driving |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
16th International Conference on Computational Science |
Abbreviated Journal |
|
|
| |
Volume |
80 |
Issue |
|
Pages |
2377-2381 |
|
| |
Keywords |
Pedestrian detection; Autonomous Driving; CUDA |
|
| |
Abstract |
We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study. |
|
| |
Address |
San Diego; CA; USA; June 2016 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCS |
|
| |
Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ CSE2016 |
Serial |
2741 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
|
| |
Title |
Harmony Potentials for Joint Classification and Segmentation |
Type |
Conference Article |
| |
Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
3280–3287 |
|
| |
Keywords |
|
|
| |
Abstract |
Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21. |
|
| |
Address |
San Francisco CA, USA |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ GBW2010 |
Serial |
1296 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
|
| |
Title |
3D Scene Priors for Road Detection |
Type |
Conference Article |
| |
Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
57–64 |
|
| |
Keywords |
road detection |
|
| |
Abstract |
Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods. |
|
| |
Address |
San Francisco; CA; USA; June 2010 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPR |
|
| |
Notes |
ADAS;ISE |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ AGL2010a |
Serial |
1302 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
|
| |
Volume |
|
Issue |
|
Pages |
3066-3072 |
|
| |
Keywords |
|
|
| |
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 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ RoS2010a |
Serial |
1303 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
|
| |
Volume |
|
Issue |
|
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 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
English |
Original Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ MVG2010 |
Serial |
1304 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
|
| |
Volume |
|
Issue |
|
Pages |
1046–1053 |
|
| |
Keywords |
|
|
| |
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 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ ARL2010a |
Serial |
1311 |
|
| Permanent link to this record |
